<figure><img src="image_168.png" /><figcaption>Figure 5: 2D planimetric differences of nine sets of observations based on ENSG mount point.</figcaption></figure>

Figure 5 shows the differences with sub-nine-centimeter levels of all ENSG@20km mount point sets. We can distinguish four sets (1, 3, 5 and 8). Sets 5 and 8 reappear again to have good results like as in the LAUR case. The values of sets (2, 4, 6, and 7) are lower than the seven-centimeter difference level. Sets 9&10 shows chaotic behavior with differences varying between 2 and 9 cm difference level. We can give the same conclusion as for the LAUR mount point. Both have a 20 km distance from the reference point. We can use them as alternative mount points if the RICE mount point is disabled.

<figure><img src="image_169.png" /><figcaption>Figure 6: 2D planimetric differences of 10 sets of observations based on RICE mount point.</figcaption></figure>

Figure 6 shows the differences with sub-five centimeter levels of all RICE@9km mount point sets. The measurement differences are stable within one centimeter for each set. These results confirm the preliminary results and ensure the continuity, stability, and reliability of this mount point for use in topographic or surveying works within a 9 km distance.

# 4 DISCUSSION

Knowing that topographic works in France are subject to the decree of September 16, 2003. The accuracy classes Land surveying needs to respect an

accuracy level depending on the objective of the project and the scale of the plan ("Arrêté du 16 septembre 2003 portant sur les classes de précision applicables aux catégories de travaux topographiques réalisés par l'Etat, les collectivités locales et leurs établissements publics ou exécutés pour leur compte - Légifrance," n.d.). It gives 10 cm accuracy class for cadastral map scale of 1/500 and 20 cm for 1/1000 to 1/2000. With this discussion, we can confirm that the RICE Centipede mount point within 10 km tends to provide a five cm accuracy level. On the other hand, the 20km mount points (LAUR and ENSG) offer a 10 cm accuracy level. Therefore, the accuracy of RTK positioning presented in this work indicates that it is possible to use it in land surveying with 1/500 – 1/1000 and 1/2000 map scale factor.

15 cm with no confirmed stability (SGC and GPTR). If the accuracy level is fixed to be 10 cm level, one should not use mount points with distances over 48 km. Nevertheless, this criterion of distance is to be reconsidered after the OUIL mount point at 19 km distance has an accuracy level above 16 cm. Furthermore, we detect for a set of observations using the LAUR@19km mount point we see a huge leap in accuracy. The problem is that there are no indicators in real-time to alert the user of such a sudden out-of-accuracy range and thus out of use. We can point to the case of two mount points ENSG and ENSG2, where both are in the same place so the same distance from the user. However, they give different levels of accuracy for broadcast corrections (ENSG-5 cm and ENSG2-10cm). Hence, there is doubt about the quality of broadcast corrections without any kind of warnings sent to users in real-time.

After all, the user can use Centipede RTK network with Low-cost GNSS receivers and SW Maps application, but the user needs to control his work. To do so we suggest having some control points in the surveying area. These control points could be the result of high accuracy GNSS receiver for example. Then periodically compare the Centipede-RTK coordinates with the control point coordinates to eliminate the out-of-use problem and verify the consistency of the accuracy level. Finally, in the case of land surveying with a map scale of 1/200 to 1/500, we cannot trust the use of LCRA dependent on Centipede-RTK without verification with control points, because the user has no reliable indication of accuracy in real time. Nevertheless, it is a good alternative to be used for small-scale maps starting from 1/1000.
# 5 CONCLUSIONS

We test the feasibility of using a low-cost GNSS receiver and antenna for land surveying based on corrections broadcast from a collaborative network of RTK mount points in France. The interface is SW Maps, a free GIS Android application for smartphones. We conclude that the nearest centipede mount point does not mean that it is the best one to use. The indicators of accuracy are not representative in real-time using the SW Map smartphone application and the mount points of the collaborative free RTK network (Centipede). The corrections sent by Centipede mount points may change dramatically without any warning message for users in real-time. The use of low-cost GNSS receivers/antennas with the Centipede-RTK network connected via SW Maps GIS free smartphone application should be controlled and verified to ensure the reliability of the corrections used for land surveying.

For Future work, we intend to automatically record the data without the user intervention in the SW Map to generate temporal series for long-term measurement. Test the differences near ENSG, ENSG2, LAUR, and OUIL mount points within less than 10 km to examine the results of our work. Use the corrections of Centipede using a high-quality receiver-antenna and compare at the same time with low-cost GNSS receiver-antenna results for the same conditions to examine if there are notable differences. Explore the possibility of using Centipede corrections as an alternative solution in RTK mode for high-quality receivers when the paid network broadcast is lost. Finally, explore the archive of Centipede RTK databases in RINEX format newly available in the RENAG databases.

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# The Impact of Data Science on Geography: A Review with Optimization Algorithms

Roberto de Oliveira Machado*ª

Universidade Nova de Lisboa, Avenida de Berna, 26-C, 1069-061, Lisboa, Portugal
roberto.machado@campus.fcsh.unl.pt

**Keywords:** Data Science, Geography, Optimization Algorithms, Supervised Learning, Systematic Review.

**Abstract:** We conducted a systematic review using the PRISMA methodology, analyzing 2,996 studies and synthesizing 41 to explore the evolution of data science and its integration into geography. Optimization algorithms were employed to enhance the efficiency and precision of literature selection. Our findings reveal that data science has evolved over five decades, facing challenges such as the integration of diverse spatial data and the increasing demand for advanced computational skills. In the field of geography, data science emphasizes interdisciplinary collaboration and methodological innovation. Techniques like large-scale spatial data analysis and predictive algorithms hold promise for applications in natural disaster management and transportation optimization, enabling faster and more effective responses. These advancements highlight data science's pivotal role in solving complex spatial problems. This study contributes to the application of optimization algorithms in systematic reviews and underscores the necessity for deeper integration of data science into geography. Key contributions include identifying challenges in managing heterogeneous spatial data and promoting advanced analytical capabilities. The intersection of data science and geography leads to significant improvements in disaster management and transportation efficiency, fostering more sustainable and impactful environmental solutions.

## 1 INTRODUCTION

Data science is an interdisciplinary field that integrates statistical analysis, machine learning, and computational techniques to extract significant insights from large and complex datasets. With the advent of big data, characterized by high volumes, velocity, and variety, data science has become essential in analyzing and interpreting vast amounts of information. Technologies such as cloud computing, artificial intelligence, and advanced algorithms facilitate data processing and analysis, enabling real-time insights and decision-making across various domains.

The application of data science in geography has immense potential to revolutionize the analysis of spatial data and address complex issues. Predictive algorithms and spatial analysis techniques can enhance natural disaster prediction, optimize transportation networks, and manage resources sustainably. These innovations provide precise and actionable information, improving the effectiveness

of spatial interventions and fostering community resilience (Yue, 2016; Singleton, 2019). However, the integration of data science into geography faces challenges, such as the need to harmonize heterogeneous spatial data, the demand for interdisciplinary expertise, and the lack of specialized tools for advanced spatial analysis, which hinder its broader adoption in the field of geography.

This study examines the evolution of data science and its integration into geography through a systematic review. It employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology to ensure a structured and unbiased synthesis of relevant studies. Additionally, supervised learning techniques, such as logistic regression and Naïve Bayes algorithms, are employed to optimize the literature selection process, reducing manual workload and improving precision.

A bibliometric analysis is conducted using Bibliometrix to explore trends, knowledge gaps, and contributions across disciplines.

ªhttps://orcid.org/0000-0002-8346-4155

Machado, R. O.
The Impact of Data Science on Geography: A Review with Optimization Algorithms.

Paper published under CC license (CC BY-NC-ND 4.0)

In *Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025)*, pages 219-230

ISBN: 978-989-758-741-2; ISSN: 2184-500X

Proceedings Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
The primary objectives of this work are to:
(i) conduct a systematic review following the PRISMA protocol, enhanced by optimization algorithms, to ensure a comprehensive analysis of existing knowledge;
(ii) examine the evolution of data science and its applications in geography; and
(iii) provide actionable insights into how data science can be leveraged to address complex spatial challenges.

By achieving these objectives, the research highlights the transformative potential of data science in addressing spatial challenges and advancing interdisciplinary collaboration.

The article is structured into five sections:
(i) systematic literature review;
(ii) bibliometric analysis;
(iii) results;
(iv) discussion; and
(v) conclusion.

## 2 METHODS

To ensure a structured and unbiased analysis of the literature, the study adhered to the PRISMA methodology (Pickering et al., 2014; Teh et al., 2020), which comprises four key stages: identification, screening, eligibility, and inclusion. Relevant studies were retrieved through comprehensive database searches in Scopus and Web of Science (WOS), employing carefully selected search terms to capture the intersection of data science and geography. Titles and abstracts were screened to exclude irrelevant articles based on predefined criteria, ensuring both relevance and methodological rigor. Full-text articles were subsequently assessed for eligibility, and only those meeting all criteria were included in the final analysis.

The screening process was enhanced through the implementation of supervised learning algorithms, such as Logistic Regression and Naïve Bayes. These algorithms automated key stages of the process, significantly reducing manual effort. Initially, additional classifiers, including Support Vector Machines, K-Nearest Neighbors, and Random Forest, were also evaluated. However, they were excluded due to their lower performance in terms of accuracy and false-negative rates. Logistic Regression exhibited the lowest false-negative rate, ensuring the inclusion of high-quality and relevant studies, while Naïve Bayes excelled in computational efficiency and generalization capability, making them suitable for handling the available dataset.

A bibliometric analysis conducted using Bibliometrix provided deeper insights into trends, knowledge gaps, and interdisciplinary contributions.

Graphs and tables were generated in Excel, while spatial data visualizations were created in Jupyter Notebook, ensuring a robust and versatile approach to data analysis.

To enhance transparency and reproducibility, rigorous inclusion and exclusion criteria were applied throughout the process. Potential biases were mitigated through manual verification (Ólafsdóttir & Tverijonaite, 2018; Saltz & Dewar, 2019; Saltz & Krasteva, 2022) of algorithmic outputs and sensitivity analyses (Kantardzic, 2019) to assess the robustness of the findings.

### 2.1 Systematic Literature Review

The review followed six steps: formulation, identification, selection, eligibility, inclusion, and categorization. Two research questions were established: "What is the state of the art in data science?" and "What is its applicability in geography?" To ensure a structured selection process, specific inclusion criteria were defined, considering articles, conference papers, reviews, books, and book chapters that contained the relevant search terms in the title, abstract, and keywords, were aligned with the research questions, and were written in English. Conversely, exclusion criteria were applied to filter out editorials, notes, short surveys, data papers, and any studies misaligned with the research questions or written in languages other than English.

The literature search was conducted in the Scopus and Web of Science (WOS) databases, using the keywords "Data Science" AND "Geography" OR "Spatial" OR "Big Data" OR "Geographic" OR "Geospatial". The search encompassed all available studies up to May 2023, focusing on abstracts to ensure relevance. The results were exported in CSV format for Scopus and Research Information Systems (RIS) format for WOS. A normalization process was then applied to merge and structure data from different sources into a unified CSV format, leveraging Python 3.8 and the Pandas library for efficient data processing.

The next phase involved the manual classification of a set of articles into relevant and irrelevant categories. This step was followed by the implementation of a supervised active learning cycle, leveraging the following libraries: Pandas; `sklearn feature extraction.text.Tfidf Vectorizer`; `sklearn linear model.LogisticRegression`; `sklearn naive bayes.MultinomialNB`; `sklearn metrics.recall score`; `sklearn metrics.confusion matrix`.
The Term Frequency - Inverse Document Frequency (TF-IDF) technique was used to measure the importance of a word in a document, considering a collection of documents or corpus. This measurement was performed by assigning a weight during the text vectorization process (Bafna et al., 2016; Chen et al., 2016; Sah et al., 2020). The TF-IDF calculation for the term 't' in a document 'd' is defined as shown in Equation (1):

$$TF - IDF(t, d, D) = TF(t, d) * IDF(t, D) \quad (1)$$

where:

* TF(t, d) corresponds to the number of times the term t appears in the document d;

* IDF (t, D) represents the inverse document frequency of the term t in the set of documents D, calculated as: IDF(t, D) = log (N/(1 + n_t))

where:

* N is the total number of documents in the set;

* n_t is the number of documents that contain the term t.

Here is the total number of documents in the set and is the number of documents that include the term t. This method assigns higher weights to less frequent terms and lower weights to more frequent terms (O'Mara-Eves et al., 2015; Chen et al., 2016).

In the selection procedure, the Logistic Regression classifier demonstrated superior performance in classification, identifying higher proportion of relevant studies compared to the total number of relevant articles detected. This indicates that Logistic Regression had a lower false-negative rate, being more effective in identifying relevant studies. The performance metrics of active learning for the two algorithms used (Logistic Regression and Naïve Bayes) in categorizing the relevance of studies related to data science concepts. The recall value was calculated using Equation (2):

$$\frac{TP}{TP + FN} \qquad (2)$$

where:

* TP represents true positives (relevant studies correctly identified) and FN represents false negatives (relevant studies incorrectly marked as irrelevant) (O'Mara-Eves et al., 2015).

Throughout the cycle, the binary Logistic Regression classifier was used to predict the category of a dependent variable based on the values of the

independent variables. The result of this process was 0 for "irrelevant" or 1 for "relevant", as shown in Equation (3):

$$P(x_i, \theta) = \frac{1}{1 + e^{-z}} \qquad (3)$$

where:

* P(x_i, θ) is the probability that observation x_i belongs to class y_i;

* e is the constant Euler's number, approximately 2.71828;

* z is the linear combination of features weighted by parameters:

$$z = \theta_0 + \theta_1 x_{i1} + \theta_2 x_{i2} + \dots + \theta_p x_{ip};$$

* $\frac{1}{1+e^{-z}}$ is the sigmoid function, which transforms the linear combination z into a value between 0 and 1, interpreted as the probability of belonging to the positive class.

Implementing a systematic literature review using supervised learning can reduce the time required to identify relevant studies. However, this method is not without biases, potentially omitting up to 5% of relevant literature (Ros et al., 2017; Yu & Menzies, 2019; Ferdinands et al., 2020; Wang et al., 2020; van De Schoot et al., 2021). To mitigate this, the process followed guidelines proposed by Yang (2018), which recommend finalizing the review process after identifying 50 irrelevant records.

After completing the screening process with the Logistic Regression classifier, an additional selection was conducted using the Naïve Bayes classifier. The Naïve Bayes classifier employs a probabilistic classification mechanism based on Bayes' Theorem (Yang, 2018), expressed by the following Equation (4) (Zhang & Gao, 2011; Jurafsky & Martin, 2019):

$$P(y|x_1, x_2, \dots, x_n) \propto P(y) \prod_{i=1}^{n} P(x_i | y) \qquad (4)$$

where:

* P(y|x_1, x_2, ..., x_n) is the conditional probability of class y given the variables x_1, x_2, ..., x_n;

* P(y) corresponds to the prior probability of class y;

* P(x_i|y) represents the conditional probability of observing the feature x_i given that the class is y;

* $\prod_{i=1}^{n} P$ indicates the multiplication of all conditional probabilities for each feature x_i;

α = 3.822 is a hyperparameter used to improve classifier performance.

The results were compared to identify potential
omissions of relevant literature. Following this, a full reading of the selected studies was conducted, with particular attention given to frequently cited authors not included in earlier phases. Relevant studies were integrated to minimize potential biases. In the eligibility phase, studies misaligned with the research questions were excluded from the review process.

In the final categorization phase, information from eligible studies was extracted. Bibliometric data were analyzed using the open-source tool Bibliometrix, which provided disaggregation by space, time, thematic area, and journals. Graphs and tables were generated in Excel, and spatial data visualizations were developed using Jupyter Notebook. It is important to acknowledge that the review was conducted by a single reviewer, which could introduce subjectivity in the inclusion and exclusion criteria. Additionally, only textual studies were considered due to limitations in applying text classifier algorithms to digitized documents. The Python code for the systematic review is well-documented and ensured for reproducibility. It is available for consultation on [GitHub] and [Google Colab].

# 3 RESULTS

The results of this systematic review provide a comprehensive overview of the main trends and challenges in applying data science to geography. The initial database search identified 2,996 studies. After removing duplicates, 1,806 studies were selected for screening. Title and abstract analysis led to the selection of 126 documents for full reading. After examination, 35 studies were included, while the rest were excluded for not aligning with the research question. Citation analysis contributed six additional studies, including three books and three other documents, two of which were in image format. The total number of studies for categorization and synthesis was 41.

## 3.1 Bibliometric Analysis

This systematic review includes studies from 10 countries. The United States leads with 24 studies, followed by the Netherlands with four, the United Kingdom and China with three each, and Australia with two. The remaining countries contributed one publication each. The reviewed studies span from 1966 to 2022, with the earliest published in 1966 and the latest in December 2022. The most representative years were 2017 (17%), followed by 2018 (15%), and

2016 and 2022 (10% each). Until 2012, studies focused on the contribution of statistics and computing to the foundations of data science. Between 2013 and 2017, research addressed the field's definition and applicability. From 2018 to 2022, the literature expanded to various topics, from specific methodologies to guidelines for knowledge extraction, with a link to geography.

Among the six most representative areas of knowledge, computer science is the most prominent, accounting for 35% of occurrences, followed by social sciences and decision sciences, each at 16%. Mathematics accounts for 14%, earth and planetary sciences for 11%, and business, management, and accounting for 8%. Regarding the distribution of study types, 46% are articles, 25% are reviews, 17% are conference papers, and 12% are books.

The most frequent periodicals include *Communications of the ACM*, *Geographical Analysis*, *Geography Compass*, and *Proceedings of the National Academy of Sciences*, each with two publications. Among the top authors, van der Aalst W. leads with three studies, while Smyth P., Arribas-Bel D., and Cao L. each have two. The six most cited studies cover topics such as data mining (Fayyad et al., 1996), process mining (van der Aalst, 2016), the intersection of supply chain management and data science (Waller & Fawcett, 2013), the challenge of defining data science and its link to big data (Provost & Fawcett, 2013), the role of data scientists (Davenport & Patil, 2012), and the distinction between data science and statistics (Dhar, 2013).

## 3.2 Definition of the Concept of Data Science

As a starting point, we provide an overview of the concept of data science as defined in previous studies. Data science is broadly conceived as a flexible and practical approach to extracting knowledge or useful patterns from data (Dhar, 2013; Provost & Fawcett, 2013; Kelleher & Tierney, 2018; Vicario, G., & Coleman, 2019). It combines three fundamental components: computing, statistics (Vicario, G., & Coleman, 2019; Blei, 2017; Cao, 2018; Yan & Davis, 2019), and domain knowledge (Song & Zhu, 2015; Muller et al., 2019). Computing facilitates large-scale parallel computing, algorithms, and data mining to uncover useful information (Provost & Fawcett, 2013; Kelleher, J., & Tierney, 2018; Donoho, 2017). Statistics provide a quantitative framework, applying concepts such as mean, standard deviation, hypothesis testing, and statistical inference (Waller, & Fawcett, 2013). Domain knowledge is crucial for
analyzing and extracting knowledge pertinent to
specific areas (Muller et al., 2019).

Vicario et al. (2019), Yan & Davis (2019), and
van der Aalst (2016) emphasize the interdisciplinary
nature of data science, where various disciplines
collaborate to extract knowledge from data. In
contrast, Song & Zhu (2019) describe data science as
multidisciplinary, with each field contributing
uniquely while operating within a common context.
Cao (2018) further classifies data science as
transdisciplinary, where different domains converge
to extract relevant information from data. Therefore,
data science can be inter-, multi-, or transdisciplinary
depending on the project's scope, the individuals
involved, and the evolving nature of the field.
Interdisciplinary and multidisciplinary approaches
keep data science closely linked to its foundational
areas, while achieving a transdisciplinary state
requires robust research that encompasses both
interdisciplinary and multidisciplinary elements.

The Venn diagram illustrates the convergence of disciplines in data science, emphasizing its interdisciplinary nature, as noted by Blei & Smyth (2017). The overlap of computer science and domain knowledge produces "advanced analysis," where models are created, and analyses are conducted. The intersection of mathematics, statistics, and computer science enables "machine learning" through data and algorithms. The overlap of domain knowledge with mathematics and statistics leads to "traditional analysis," using classical methods to identify patterns (Provost & Fawcett, 2013; Kelleher & Tierney, 2018). At the core, where all three disciplines intersect, lies the discovery of knowledge through integrated data analysis, statistical methods, and advanced computing.

Song & Zhu propose a distinct perspective,
suggesting that data science comprises three pillars:
data, technologies, and people. They argue that the
third pillar —skilled professionals — is essential for
selecting appropriate data and applying advanced
technologies in analysis. Blei & Smyth (2017) also
emphasize the human element in data science,
highlighting the need for data scientists to understand
the problem domain, devise inferential methods, and
effectively use computational tools. Scheider et al.
(2020) further note that such skills are often acquired
through professional interactions and practical
implementation, rather than solely through academic
training (Hicks & Irizarry, 2018).

The proliferation of data, driven by numerous
human activities and technological advancements,
represents a significant revolution in knowledge
production (van der Aalst, 2016). Data science has

emerged as a flexible field capable of analyzing large
volumes of structured and unstructured data.

Structured data, typically stored in SQL, are
tabulated for easier storage and processing, while
unstructured data, in NoSQL systems, are more
irregular and complex to analyze (Kelleher &
Tierney, 2018). To manage this complexity, data
scientists often create an *n*m analytical matrix where
'n' represents entities (rows) and 'm' represents
attributes (columns). This matrix, incorporating data
from multiple sources such as databases, data
warehouses (Kelleher & Tierney, 2018), big data
lakes (Carniel & Schneider, 2021), online content,
and social media streams, is fundamental to data
science projects. Significant time and resources are
spent organizing, cleaning, and updating this matrix.

## 3.3 Origin and Evolution of the Concept

In the 1960s, statistician John Tukey and computer
scientist Peter Naur laid the foundations of data
science, leading many to view it as a descendant of
statistics and computer science (Blei & Smyth, 2017).
While incorporating methods and approaches from
these fields, data science has evolved to meet
contemporary analytical needs driven by the
omnipresence of data collected ubiquitously (van der
Aalst & Damiani, 2015). Over the past two decades,
this evolution has consolidated data science as a
distinct science.

The term “data science” first appeared in literature
in the preface of Peter Naur's book *Concise Survey of
Computer Methods* in the 1970s, where he defined it
as the "science of dealing with data" (Cao, 2018, p.
9). However, John Tukey's earlier work in 1962
proposed incorporating data analysis processes
within statistics, advocating for the inclusion of new
theories, computers, visualization tools, and larger
datasets (van der Aalst, 2016; Cao, 2018). This need
for change was echoed in the field of computing.

In 1966, Peter Naur introduced “Datalogy” (Naur,
1966), the systematic study of data, including
representation and automatic processing
mechanisms. Naur characterized this field as broad
and interdisciplinary, essential for tasks across
different domains and data types. Despite its
significance, the role of statistics in defining data
science foundations remains debated, as statisticians
traditionally focused more on formulating theories
than addressing practical challenges associated with
large datasets and computational complexities (van
der Aalst, 2016).

In the 1970s, John Tukey's *Exploratory Data*
*Analysis* (Tukey, 1997) introduced techniques aimed at improving results through hypothesis testing, unbiased data, and conventional statistics, which later came to be known as confirmatory data analysis. Concurrently, the relational data model SQL revolutionized database information storage, indexing, and retrieval (Kelleher & Tierney, 2018), laying the groundwork for “Knowledge Discovery in Databases” (KDD) in the late 1980s (Cao, 2018). The seminal 1996 article "Data Mining and Knowledge Discovery in Databases" by Fayyad et al. (1996) outlined a five-stage process for knowledge discovery: selection, pre-processing, transformation, data mining, and pattern interpretation.

Building on the KDD model, other methodologies emerged both in academia and industry due to the growing demand for data analysis tools. Notable among these was the Cross-Industry Standard Process for Data Mining (CRISP-DM), organized around a six-stage lifecycle, providing a flexible and iterative framework for data mining projects (Martínez-Plumed, 2021). As data science gained prominence in the 1990s, it became increasingly associated with both research and practical applications (Zhu & Xiong, 2015).

The early 21st century witnessed further advancements, including Cleveland's action plan for teaching data science and the launch of NoSQL databases, propelling the field forward. The increasing of systems led to the rise of big data, creating the need for new processing frameworks such as MapReduce and Hadoop (van der Aalst, 2016; Cao, 2018). MapReduce, with its Map and Reduce functions, and Hadoop, integrating Hadoop Distributed File System (HDFS) with MapReduce, enabled efficient storage and distribution of large data volumes across clusters (Kumar & Mohbey, 2022).

In the second decade of the 21st century, data science became more clearly defined, integrating computer science, mathematics, statistics, and domain-specific knowledge (Brady, 2019). The role of the data scientist, as highlighted by Davenport & Patil (2012), gained prominence as data science became critical for problem-solving and deriving insights. This period saw an increase in publications on data science infrastructure and processes, such as van der Aalst's *Process Mining Data Science in Action* (2016) and Donoho's concept of “greater data science” activities (2017). The profession of data scientist emerged as both essential and desirable, although discussions on the competencies required for these professionals continue to evolve in literature and academia (Vicario & Coleman, 2019).

## 3.4 Function and Applicability

Our analysis demonstrates that data science has solidified its position as a key discipline for solving complex problems through the integration of statistical and computational methods (Cleveland, 2001; Waller & Fawcett, 2013; Martínez-Plumed et al., 2021). In addition to addressing the challenges posed by the big data phenomenon (Song, & Zhu, 2015), data science aims to generate both actional and actionable knowledge (Donoho, 2017). It employs automated analyses to understand various phenomena, converting data into actionable insights, diagnostics, and automated decisions (Provost & Fawcett, 2013; van der Aalst, 2017). This transformation follows a methodology that ranges from data collection to knowledge acquisition (Cao, 2018). As Brady (2019) notes, data science employs specific processes to explore and describe data, identify meaningful patterns, and present them effectively, adapting and redefining the traditional objectives of statistics and computer science. Van der Aalst & Damiani (2015) emphasize that data science is capable of addressing four fundamental questions: "What happened?" "Why did it happen?" "What will happen?" and "What is the best decision to make?" Notably, although data science is often associated with large datasets, it is equally effective when applied to smaller datasets, which can also yield valuable information for solving specific problems.

Most studies suggest that the knowledge discovery process in data science follows a lifecycle encompassing systematic methodologies, open and interdisciplinary approaches, and synergies from various disciplines (Song, & Zhu, 2015; van der Aalst & Damiani; 2015; Cao, 2017; Yu & Kumbier, 2020). However, there is some discrepancy concerning the number of stages in this cycle, ranging from four (van der Aalst, 2017) to eight stages (Yu & Kumbier, 2020). The most referenced model is CRISP-DM (Provost & Fawcett, 2013; Song & Zhu, 2015; Martínez-Plumed, 2021). Its main strength lies in its independence from specific software or analysis techniques (Kelleher, & Tierney, 2018). CRISP-DM, an extension of the KDD model (Martínez-Plumed, 2021), is structured into six stages: (i) understanding the project objectives and defining the problem; (ii) collecting data for analysis; (iii) performing cleaning actions to correct or remove inaccurate or irrelevant information; (iv) applying algorithms to identify useful patterns in the data and establish representative models; (v) evaluating the model and the developed stages; and (vi) implementing the model, assessing its practical viability and ability to generate knowledge
or value from the data (Larose & Larose, 2014). It is
important to note that the CRISP-DM process is
iterative, not necessarily following a linear sequence
of stages.

The technologies used in data science vary across
organizations and projects (Kelleher & Tierney,
2018). As the size of the organization or the volume
of data increases, so does the complexity of the
ecosystem to be implemented. A typical data science
ecosystem comprises tools and components from
multiple software vendors, capable of processing data
in diverse formats. Commonly used tools include
SQL and NoSQL databases (van der Aalst &
Damiani, 2015), MapReduce for distributed
computing (Blei & Smyth, 2017), and Hadoop for
storage, along with associated components such as
HBase, Hive, Pig, Mahout, Storm, and Spark (Song,
& Zhu, 2015; van der Aalst & Damiani, 2015; Kumar
& Mohbey, 2022; Abdalla, 2022). These components
are critical during the data representation and
computation phase (Donoho, 2017). The
programming languages SQL, Python, and R are
employed for querying, visualization, data cleaning,
and calculations (Brunner & Kim, 2016).

Among the various tools available, Jupyter
Notebook has gained significant prominence as an
open-source web-based interactive development
environment. Jupyter Notebook stands out for its
interface, which facilitates communication,
abstraction, and data manipulation (Carniel &
Schneider, 2021). It enables users to access databases,
configure, and organize workflows for scientific
computing and machine learning with ease.
Moreover, Jupyter Notebook is highly flexible,
allowing seamless integration of extensions, such as
Python and R libraries, for analysis, model
application, and result visualization. This flexibility
and ease of use contribute to a reduction of the time
required for analysis. One of the tool's most valuable
features is its ability to integrate both text and code in
a single location, a concept known as "literate
programming". This is particularly beneficial in data
science projects involving multidisciplinary teams,
where not all members may possess a deep
understanding of all tasks, thereby making integrated
documentation an essential feature.

3.5 Relationship with Geography

Data science emerges in geography as a
complementary, though somewhat limited, process
(Singleton & Arribas-Bel, 2019). A review of the
literature analysis reveals no consensus on the
definition of the term, with various designations,

including "earth data science," "geospatial data
science," "spatial data science," and "geographic data
science."

Yue et al. (2016, p. 84) define "earth data science" as "the ability and knowledge to apply the data science paradigm to EO data". Xie et al. (2017, p. 3) describe "geospatial data science" as "the science of data-driven approaches focusing on the geospatial domain". Carniel et al. (2021, p. 1) define "spatial data science" as a "subclass of object-oriented data science and spatial data". Finally, Andrienko et al. (2017, p. 15) refer to "geographic data science" as a field for working "with data that incorporates spatial and often temporal elements".

Spatial data science is defined as the discipline
that explores data-driven approaches, with a
particular emphasis on the spatial domain. Therefore,
this review adopts the term "spatial data science",
which, according to Singleton Singleton & Arribas-
Bel (2019) and Bowlick & Wright (2018), has the
potential to revitalize quantitative geography and
geocomputation methodologies. This revitalization
occurs through the application of advanced
techniques for collecting, preparing, exploring, and
visualizing large datasets, alongside the use of
machine learning and spatial statistical methods to
detect patterns and model spatial dependencies (Xie
et al., 2017). Spatial data science seeks to extract
valuable information about space (Andrienko et al.,
2017), time, and distance (Carniel, & Schneider,
2021). High-performance computational procedures,
algorithms, and specific programming languages are
essential for extracting this information within an
interdisciplinary or transdisciplinary framework.

One of the most compelling applications of data
science in geography is the integration of EO data
with socio-economic datasets. Recent studies have
demonstrated how combining EO data with social
media and economic data enhances our understanding
of socio-economic-environmental systems, thus
facilitating improved urban planning and disaster
management (Yue et al., 2016).

Visualization tools are essential in geographic
data science. For instance, the Urban Space Explorer
tool uses a coordinated multiple-view interface to
analyze social media posts in relation to population
distribution and points of interest. This tool has
proven invaluable to urban planners, providing
insights into urban dynamics and supporting data-
driven decision-making (Xie et al., 2017).

Xie et al. (2017) view spatial data science as
transdisciplinary, addressing the limitations of
isolated data mining and machine learning
approaches by integrating four key elements:
mathematics, statistics, computer science, and geospatial techniques. Palomino et al. (2017) argue that spatial data science is inherently interdisciplinary, emerging from the intersection of three distinct fields: Geographic Information Science, data science, and CyberGIS, which is defined as "GIS based on advanced cyberinfrastructure and e-science" (VoPham et al., 2018, p. 2). Carniel & Schneider (2021) also regard spatial data science as interdisciplinary, rooted in three core disciplines: mathematics/statistics, computer science, and business information/domain specialization. However, the literature lacks clarity on the specific techniques employed within the subclasses that emerge from the intersection of these core disciplines in spatial data science.

The integration of the three core disciplines of data science — computer science, mathematics and statistics, and domain knowledge —, can be effectively visualized using a Venn diagram. In the context of mathematics and statistics, techniques such as Kriging, bootstrap, and autocorrelation are employed. In computer science, programming and managing large spatial datasets are crucial, utilizing tools such as Spatial Hadoop and specialized open-source libraries like PySal, PyMove, Geopandas, and Moving Pandas. In domain knowledge, comprehending the spatial context — including location, distance, and spatial and temporal interactions — is crucial.

The implementation of data science projects within spatial contexts is often underemphasized in the literature. Palomino et al. (2017) and VoPham et al. (2018) advocate for workflows utilizing open-source and cloud computing technologies. Palomino et al. (2017) propose a comprehensive framework consisting of two complementary models: a four-stage model—setting up the work environment, data management (collection, cleaning, and transformation), analysis and visualization, and data publication, with an emphasis on collaborative work—and an iterative six-stage workflow, which builds on the previous model by adding specific steps for data import, processing, cleaning, transformation, visualization, modeling, and communication of results. Carniel & Schneider (2021) introduce a life cycle based on conventional data science workflows, comprising four components: problem definition and understanding, spatial data modeling and understanding, spatial reasoning, and spatial data visualization.

In practice, spatial data science benefits from the inherent flexibility in implementing data science projects. While establishing models, such as CRISP-

DM, are widely used, scientists retain the flexibility to adapt the lifecycle or workflow to suit the specific project context (Martínez-Plumed et al., 2021). The skills required for spatial data science continue to evolve, with teamwork, machine learning techniques, cloud computing, and proficiency in programming languages such as Python, R, and SQL being crucial (Jiang & Chen, 2021).

Successfully executing a spatial data science project requires proficiency in three key areas: domain knowledge, mathematical and statistical skills, and computer science (Carniel & Schneider, 2021; Bowlick & Wright, 2018). However, the role of quantification in geography remains a subject of ongoing debate, in part due to the fact that many practitioners lacking a strong background in statistics and computing. Increasing emphasis on these areas is crucial for strengthening the connection between geography and data science, both academically and professionally.

There is a longstanding tradition of using open-source programming languages such as Python and R, in spatial analyses (Anselin & Rey, 2022). The CyberGIS domain advocates for the application of cloud computing in geography (Palomino et al., 2017). Specialized software, such as QGIS and ARCGIS, has adapted to this trend by integrating open-source extensions for querying, analyzing, and visualizing data. In recent decades, geocomputation and remote sensing have increasingly incorporated large datasets, algorithms, and open-source code into their analyses (Arribas-Bel & Reades, 2018). Geographers are also actively involved in smart city projects, which presents new opportunities for analyzing large datasets (Scheider et al., 2020).

In these environments, structured and unstructured data from diverse sources are frequently employed for complex analyses (Brady, 2019). This convergence of factors, coupled with ongoing developments, can cultivate a stronger relationship between data science and geography. To date, this relationship has remained fragmented and indirect (Singleton, & Arribas-Bel, 2019). Should this relationship deepen, it could mark a new chapter for the discipline, reinforcing its position in a data-driven world, open science (Palomino et al., 2017), and cloud computing. This scenario is consistent with contemporary data science approaches to data collection, transformation, processing, and analysis.
# 4 DISCUSSION

## 4.1 Systematic Review

This study employed an efficient, transparent, and reproducible process for bibliographic selection, providing a detailed and technical overview of the methodologies and algorithms used. Mathematical equations and detailed discussions on algorithm performance were provided, along with well-documented Python code that can be easily adapted and implemented both locally and on web-based platforms.

However, several limitations were encountered in implementing Python pipelines for continuous bibliography updates. The Scopus and Web of Science (WOS) APIs imposed functional restrictions, limiting the number of requests and records that could be retrieved and increasing query complexity. Consequently, article selection required direct interaction with the platforms. Furthermore, WOS does not allow direct CSV data export, requiring the conversion of RIS files to CSV using Python. This process introduces potential errors and inconsistencies, requiring rigorous standardization and cleaning procedures to ensure data quality.

## 4.2 Data Science

Data science plays a pivotal role in addressing complex problems by utilizing statistical and computational methodologies. In the era of big data, characterized by increasing volume, velocity, and variety, data science has evolved to automate and enhance data analysis processes, thereby improving diagnostics, predictive analytics, and decision-making tools.

By redefining traditional statistical and computational objectives, data science generates actionable insights across various domains, highlighting its transformative potential in addressing contemporary challenges.

## 4.3 Application in Geography

In geography, data science emerges as a complementary yet underutilized tool. The lack of consensus surrounding terms such as "earth data science," "geospatial data science," and "spatial data science" highlights the need for standardized terminology. Among these, "spatial data science" has gained prominence as a promising framework to rejuvenate quantitative geography and geocomputation methodologies.

Advanced techniques, such as machine learning and predictive analytics, offer significant potential across spatial domains. For instance, predictive algorithms enhance natural disaster management by providing more accurate forecasts and facilitated effective response strategies. The analysis of large datasets optimizes transportation routes, reduces congestion, and improves logistical efficiency. Furthermore, data science plays a crucial role in sustainable resource management by identifying patterns and trends that foster the adoption of environmentally friendly practices.

## 4.4 Challenges and Ethical Considerations

The application of data science in geography presents significant challenges, including the integration of heterogeneous spatial data, the need for advanced computational and statistical expertise, and ethical concerns surrounding data privacy and usage. However, spatial data science offers innovative solutions by integrating mathematics, statistics, computer science, and geospatial techniques. Tools such as open-source software and cloud computing provide promising pathways for addressing these challenges.

## 4.5 Interdisciplinary Collaboration

Interdisciplinary collaboration among geographers, data scientists, statisticians, and ethicists is crucial for addressing complex challenges at the intersection of geography and data science. Previous studies have shown that multidisciplinary teams optimize the analysis of large spatial datasets. Recommended strategies include forming diverse teams, fostering advanced data analysis skills, and adhering to rigorous ethical standards to promote methodological innovation and ensure sustainable solutions.

## 4.6 Limitations and Future Directions

Future research should prioritize the integration of advanced data science techniques into geography to tackle complex spatial challenges. Key areas include leveraging machine learning algorithms and incorporating novel data sources, such as Internet of Things (IoT) sensors, for real-time dynamic analyses. However, the adoption of these techniques is hindered by the limited proficiency of many geographers in programming and computational tools, which restricts the effective application of these methods. Bridging this gap will necessitate
interdisciplinary collaboration and targeted training
programs that align technological advancements with
practical needs.

Emerging technologies, including artificial
intelligence and cloud computing, hold
transformative potential for spatial research. These
technologies enhance data processing capabilities,
facilitate the integration of large datasets, and
improve analytical precision. To fully capitalize on
these benefits, it is essential to address skill gaps and
develop user-friendly tools tailored to non-specialists.
By overcoming these challenges, we can enable
innovative, sustainable solutions for critical
geographic issues, such as disaster management,
urban planning, and resource optimization.

5 CONCLUSIONS

This study highlights the application of supervised
learning algorithms, specifically Logistic Regression
and Naïve Bayes, to optimize the systematic review
process. These techniques notably increased
efficiency and precision, while reducing the time
required for literature selection, thereby
demonstrating the transformative potential of data
science methodologies in systematic reviews. By
showcasing these improvements, the study illustrates
how data science approaches can effectively enhance
various research processes.

The investigation reveals that the majority of data
science studies were conducted in the United States
of America. This is largely attributed to country's
advanced technological infrastructure, prestigious
academic institutions, substantial financial
investment in research, supportive policies for
technological development, and the presence of major
technology companies that drive innovation and the
application of data science. In the field of geography,
this infrastructure and support have facilitated the
precise and efficient analysis of large spatial datasets.
Advanced methodologies, including machine
learning and data mining, hold significant potential to
improve natural disaster management, optimize
transportation routes, and promote sustainable
resource management.

However, several significant challenges remain.
The need for advanced computational and statistical
skills, alongside ongoing ethical concerns regarding
privacy and data usage, continues to present
obstacles. Overcoming these challenges requires an
interdisciplinary approach, integrating expertise from
various domains to develop robust and ethical
solutions.

This study highlights several practical
implications for applied geography:

1. **Natural Disaster Management:** Predictive algorithms can substantially improve the accuracy of natural disaster forecasts, enabling more effective and timely evacuation plans and resource allocation. Enhanced disaster management can save lives, reduce economic losses, and strengthen community resilience.

2. **Transportation Optimization:** Data science techniques can facilitate the design of more efficient transportation networks, reducing congestion and improving logistics. Optimized transportation can lead to better urban mobility, reduced environmental impact, and a higher quality of life for city residents.

3. **Sustainable Resource Management:** By identifying patterns and trends in resource use, data science can promote sustainable practices. Effective resource management ensures the better conservation of natural resources and supports environmental sustainability.

In summary, data science offers new perspectives
and solutions to complex geographical challenges.
This study underscores the importance of
interdisciplinary collaboration and methodological
innovation in harnessing the transformative potential
of data science within geography, offering a solid
foundation for future research and practical
applications.

ACKNOWLEDGMENT

This work was supported by the Fundação para a Ciência e Tecnologia [UI/BD/151395/2021].

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Surface Current Visualization in Waterway Based on Mike 21 Model
and S-100 Standards

Zixuan Wang, Mingyang Pan*, Shaoxi Li, Chao Li and Zongying Liu

Navigation College, Dalian Maritime University, Dalian, Liaoning, 116026, China
panmingyang@dlmu.edu.cn

Keywords: S-100, Mike 21, Surface Current, Marine Data Standards.

**Abstract:** The S-100 standard, proposed by the International Hydrographic Organization (IHO), aims to address the limitations of the S-57 standard in terms of data application and interoperability. This study focuses on the Fujiangsha water area, using the Mike 21 hydrodynamic model to simulate the flow dynamics in the region. The standardized processing of this data generates S-111 surface current data that complies with the S-100 specifications. The visualization of surface current data is realized using Cesium, which displays the flow field characteristics of the Fujiangsha water area. By integrating the standardization of S-111 data with visualization technology, this paper seeks to provide new technical support and application demonstrations for maritime management and navigation safety, while exploring the potential for further development of the S-100 standard in practical applications.

# 1 INTRODUCTION

The S-100 Universal Hydrographic Data Model is a new generation of marine geographic information standard officially established by the International Hydrographic Organization (IHO) in 2010. It aims to address the limitations of the S-57 standard in terms of data application and interoperability, facilitating data fusion and sharing (Luo, J. N. et al., 2019). S-100 covers electronic charts and various marine environmental information data products, enhancing the visualization and application of dynamic hydrological data. Its product specifications (such as S-101 electronic charts, S-102 bathymetric surfaces, S-111 surface currents, etc.) provide technical support for navigation safety and efficiency, becoming a crucial foundation for modern marine surveying and mapping(Wu, L. L. et al., 2019).

However, current research on S-100 remains largely theoretical (Peng, W. et al., 2017; Dou, H. X., 2013; Liu, Q. C., 2012), with limited studies focused on its practical applications, particularly in the area of channel information visualization. To address this gap, this paper takes the Fujiangsha water area as the study subject, generating surface current data that complies with the S-111 standard through

standardized processing. These data are derived from simulations of the channel flow field using the Mike 21 hydrodynamic model, which provides hydrodynamic characteristic data of the region, including high-resolution data on water flow speed, direction, and other parameters. To further demonstrate and apply these data, a front-end platform based on Cesium was developed, and the surface current data were visualized, providing an intuitive representation of the flow field characteristics in the Fujiangsha water area.

# 2 METHODOLOGY

## 2.1 S-111 Data Structure

Surface current data in the S-111 standard can be represented in two formats: point data contained within a regular grid and point sets described by an irregular grid. Based on the source of surface current data, it can be categorized into four basic types: observed or predicted values at multiple fixed locations, predicted values arranged in a regular grid, values at multiple locations but not within a regular grid, and values observed at mobile stations. In this

<footnote>* Corresponding author</footnote>
study, the surface current data type is predicted values arranged in a regular grid.

The S-111 regular grid is an implementation of S100_GridCoverage. A regular grid is a two-dimensional orthogonal spatial grid defined by several attributes, including the grid origin, spacing, and grid indices. The velocity and direction of surface currents correspond to the nodes within the regular grid. The grid values are stored sequentially along the X-axis at the lowest position of the Y-axis, starting from the leftmost value and proceeding to the next X-axis values for each subsequent Y-axis position until the top of the Y-axis is reached. A typical regular grid and its parameters are illustrated in the figure 1.

<figure><img src="image_170.png" /><figcaption>Figure 1: Typical structured grid and its parameters.</figcaption></figure>

According to the S-111 Surface Current Product Specification of the IHO-released S-100 series product standards, surface current data products must be encoded using the Hierarchical Data Format version 5 (HDF5). The HDF5 data format is capable of efficiently organizing massive amounts of data through its inherent structural features. Typically, files adopt a hierarchical tree structure, where the nodes of the tree are Groups, representing collections of objects. Each group contains one or more Datasets, which are multidimensional data arrays that include attributes and other metadata. The structure of an HDF5 file is shown in the figure 2.

<figure><img src="image_171.png" /><figcaption>Figure 2: HDF5 file structure.</figcaption></figure>

According to the mandatory naming conventions outlined in the S-100 standard, the following groups and data names are required in the S-111 data product: Group_F, featureCode, SurfaceCurrent, axisNames, Positioning, SurfaceCurrent.nn, and Group_nnn (n is an integer from 0 to 9). The figure 3

illustrates the structure of example S-111 data provided by the UK Hydrographic Office.

<figure><img src="image_172.png" /><figcaption>Figure 3: Structure of the UK Hydrographic Office's S-111 example data</figcaption></figure>

## 2.2 S-111 Visualization Representation

The S-111 Surface Current Product Specification provides detailed guidelines for the display of surface current data. The grid data of the surface current field is depicted using multiple arrows. These arrow symbols are created using Scalable Vector Graphics (SVG) instructions, following the input specifications shown in the figure 4, and are scaled according to the surface current speed and the display area.

<figure><img src="image_173.png" /><figcaption>Figure 4: Input specifications for S-111 arrow symbols.</figcaption></figure>

The direction of the arrow symbols must represent the direction of the surface current flow (referencing true north). The color of the arrows must be based on the speed values of the data, with the opacity adjusted according to the background chart. The size of the arrows must be a function of the surface current speed, where $H_{ref}$ and $S_{ref}$ are used as reference values for scaling the arrows. The minimum and maximum speed values, $S_{low}$ and $S_{high}$, are used to calculate the length of the arrows. The surface current with a speed of $S$ is represented by an arrow with a height of $H$. The calculation method is as follows:

$$H = H_{ref} \cdot \min\{\max(S_{low}, S), S_{high}\} / S_{ref} \quad (1)$$

The arrow symbols are placed over a geographically referenced background, and when the
cursor hovers over the vector arrows, the corresponding speed and direction values for each arrow are displayed. The figure 5 shows an example of S-111 data from the UK Hydrographic Office, visualized using the KHOA S-100 Viewer.

<figure><img src="image_174.png" /><figcaption>Figure 5: Visualization of the UK Hydrographic Office's S-111 example data.</figcaption></figure>

## 2.3 Mike 21 Hydrodynamic Model

Mike 21 is a two-dimensional numerical model developed by the Denmark Hydraulic Institute, providing a comprehensive and efficient design environment for engineering applications, coastal management, and planning. Mike 21 is not only beneficial for simulating complex river channels but also supports a variety of control structures. Its relatively short computation time significantly improves computational efficiency(Li, X. B., 2024). Thus, this study uses the Mike 21 to simulate the channel flow field and further construct the hydrological characteristic dataset for the channel flow field.

Mike 21 neglects vertical flow acceleration and focuses on vertically averaged flow factors. It can use either Cartesian or spherical coordinates, and in the plane, it employs unstructured grids to simulate water level and flow variations caused by various forces, or to model two-dimensional free-surface flows that disregard stratification. The numerical method used in Mike 21 is the finite volume method, which computes the normal fluxes by establishing a unit hydraulic model along the outer normal and solving the one-dimensional Riemann problem(Lü, Z. Y., 2024). The specific equations of the two-dimensional shallow water equations in the Mike 21 are as follows:

$$ \frac{\partial h}{\partial t} + \frac{\partial h\bar{u}}{\partial x} + \frac{\partial h\bar{v}}{\partial x} = hS \quad (2) $$

Momentum Equation in the X Direction:

$$ \frac{\partial h\bar{u}}{\partial t} + \frac{\partial h\bar{u}^2}{\partial x} + \frac{\partial h\bar{v}}{\partial y} = f\bar{v}h - gh\frac{\partial \eta}{\partial x} - \frac{h}{\rho_0}\frac{\partial p_a}{\partial x} - \frac{gh^2}{2\rho_0}\frac{\partial \rho}{\partial x} + \frac{\tau_{sx}}{\rho_0} - \frac{\tau_{bx}}{\rho_0} - \frac{1}{\rho_0} \left( \frac{\partial S_{xx}}{\partial x} + \frac{\partial S_{xy}}{\partial y} \right) + \frac{\partial}{\partial x}(hT_{xx}) + \frac{\partial}{\partial y}(hT_{xy}) + hu_sS \quad (3) $$

Momentum Equation in the Y Direction:

$$ \frac{\partial h\bar{v}}{\partial t} + \frac{\partial h\bar{v}^2}{\partial y} + \frac{\partial h\bar{u}v}{\partial x} = f\bar{u}h - gh\frac{\partial \eta}{\partial y} - \frac{h}{\rho_0}\frac{\partial p_a}{\partial y} - \frac{gh^2}{2\rho_0}\frac{\partial \rho}{\partial y} + \frac{\tau_{sy}}{\rho_0} - \frac{\tau_{by}}{\rho_0} - \frac{1}{\rho_0} \left( \frac{\partial S_{yx}}{\partial x} + \frac{\partial S_{yy}}{\partial y} \right) + \frac{\partial}{\partial x}(hT_{xy}) + \frac{\partial}{\partial y}(hT_{yy}) + h\nu_sS \quad (4) $$

The average flow velocity along the water depth direction is defined by the following equation:

$$ h\bar{u} = \int_{-d}^{\eta} u dz, h\bar{v} = \int_{-d}^{\eta} v dz \quad (5) $$

Where $\eta$ is Riverbed elevation; $u, v$ is velocity component in the direction $x, y$; $u_s, v_s$ is average water flow velocity in the direction $x, y$; $f$ is coriolis force coefficient; $\omega$ is Earth's rotational angular velocity; $\phi$ is local latitude; $g$ is gravitational acceleration; $S$ is flow generated by source and sink terms; $\tau_{sx}, \tau_{bx}, \tau_{sy}, \tau_{by}$ is components of the surface wind stress and riverbed bottom friction stress along the direction $x, y$; $T_{ij}$ is lateral stress terms.

The boundary condition treatment in the MIKE 21 includes open boundaries, closed boundaries, and dry-wet boundaries. Under open boundary conditions, water flow is allowed to enter and exit the boundary region. For closed boundaries, all velocity components perpendicular to the boundary are set to zero, defining a no-slip boundary. In dry-wet boundary conditions, the cells are classified as dry, semi-dry, or wet, with the conditions being satisfied as: $h_{dry} < h_{flood} < h_{wet}$ (Hu, X. W., 2024).
# 3 EXPERIMENTS

## 3.1 Two-Dimensional Flow Field Calculation

The Fujiangsha Waterway is located opposite the Zhangjiagang Port area. In this study, MIKE 21 is used to construct a two-dimensional hydrodynamic numerical model for the river section where the Fujiangsha Waterway is located. The river section is approximately 43 km long. The computational range of the model is shown in the figure 6.

<figure><img src="image_175.png" /><figcaption>Figure 6: Model computation range.</figcaption></figure>

In constructing the model computational grid, it is necessary to extract the riverbank and water depth data. In this study, ArcGIS Pro was used to extract riverbank and water depth data from the S-57 charts. Given the characteristics of the river channel, an unstructured triangular mesh grid was employed. The grid near the riverbanks and islands was refined, while the grid in the center of the river was coarser, ensuring the model's stability and improving computational efficiency. The computational grid consists of 1,277 nodes, which is shown in the figure 7.

<figure><img src="image_176.png" /><figcaption>Figure 7: Model grid division.</figcaption></figure>

The MIKE 21 model adopts a cold start for its initial conditions. The upper and lower boundary conditions are defined as water level boundaries, with the boundary format varying over time and along the boundary. In this study, the default value of 0.28 is used for the eddy viscosity coefficient. The Manning coefficient is selected to control the bed roughness, set to 32 in this study. The time step is set to 300 seconds, with results output every 30 minutes. The model's computational results are shown in the figure 8.

<figure><img src="image_177.png" /><figcaption>Figure 8: Model computation results (time interval of 4).</figcaption></figure>

## 3.2 S-111 Data Generation and Visualization

Based on the results from MIKE 21, this study uses a script to perform interpolation and gridding of flow velocity and direction data, generating surface current data files compliant with the S-111 standard. The domain of the regular grid is defined by the latitude range from about 31.93 N to 32.08 N and the longitude range from about 120.24 E to 120.70 E, with a grid resolution of 0.001. The interpolation method is used to map irregularly distributed data points onto a regular latitude-longitude grid. Subsequently, the gridded data is further processed to generate a surface current data file in HDF5 format. The interpolated data file is loaded, and the flow velocity and direction data are reconstructed into two-dimensional arrays. Geographic boundaries, resolution, and grid dimensions are then defined, ensuring consistency with the settings in the interpolation step. The resulting S-111 surface current is shown in the figure 9.

<figure><img src="image_178.png" /><figcaption>Figure 9: Generated S-111 surface current data.</figcaption></figure>

Based on the Cesium platform, this study develops a frontend platform for data reading and visualization, allowing complex current velocity and direction data to be presented in an intuitive manner. The platform reads the HDF5 files, parses the current velocity and direction data into two-dimensional arrays, and converts them into vector format. The display of arrows is defined according to the S-111 surface flow product specifications, including parameters such as color, size, and direction. The visualization of the S-111 surface current is shown in the figure 10.
<figure><img src="image_179.png" /><figcaption>Figure 10: Visualization of S-111 surface current data (a) and its zoom-in (b).</figcaption></figure>

## 4 CONCLUSIONS

This study focuses on the Fujiangsha and successfully generates surface current data compliant with the S-111 standard, based on the S-100 framework and the Mike 21 hydrodynamic model. The data is visualized using Cesium for intuitive display. The research results demonstrate that the integration of standardization and visualization techniques provides a clearer representation of the hydrological characteristics in complex water areas, offering support for improving channel management and navigation safety. However, it should be noted that the S-100 standard has not yet been officially implemented, and the current study area lacks validated observational data. Future research could refine the MIKE 21 model parameters using actual hydrological measurements when available, thereby enhancing the accuracy of simulation results. The standardized data processing framework and visualization scheme proposed in this study have demonstrated applicability to compliant hydrological data, which may serve as a reference for establishing operational systems in standardized maritime environments.

## ACKNOWLEDGEMENTS

We would like to thank the National Natural Science Foundation of China (NSFC) [grant number 52371363] for their funding.

## REFERENCES

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# Assessment of Fine-Tuned Canopy Height Maps from Satellite Imagery:
A Case Study in the Czech Republic

Leonidas Alagialoglou<fnref n="1" /> IDa, Ioannis Manakos<fnref n="2" /> IDb, Olga Brovkina<fnref n="3" /> Idc, Jan Novotný<fnref n="3" /> Idd and Anastasios Delopoulos<fnref n="1" /> IDe

**Keywords:** Canopy Height Estimation, Deep Learning, Fine-Tuning, Forest, Sentinel-2, Data-Centric AI, Uncertainty Estimation, Tree Species, Airborne Laser Scanning.

**Abstract:** This study evaluates the performance of a lightweight convolutional Long Short-Term Memory (ConvLSTM)-based deep learning model for estimating canopy height across three test areas in the Czech Republic using Sentinel-2 time series data. The model, initially trained on forest data from Germany and Switzerland, incorporate uncertainty quantification techniques and was fine-tuned and evaluated using dense airborne laser scanning (ALS) data collected between 2022 and 2024. Results show that fine-tuning reduced mean absolute error (MAE) from 4.26 m to 2.74 m in the primary test area, with similar improvements across other regions. Species-specific uncertainties were also analyzed, highlighting performance variations between deciduous and coniferous forests.

## 1 INTRODUCTION

Accurate estimation of forest canopy height is crucial for understanding forest structure, carbon storage potential, and biodiversity (Zhang et al., 2016; Mao et al., 2019; Lang et al., 2023). Traditionally, high-resolution airborne laser scanning (ALS) has been the benchmark for obtaining accurate canopy height data (Brovkina et al., 2017; Douss and Farah, 2022; Fischer et al., 2024). However, ALS is resource-intensive and limited in spatial and temporal coverage. The alternative approaches are developing that make use of readily available satellite data (Potapov et al., 2021; Lang et al., 2023; Tolan et al., 2024). Recent advancements in deep learning models have enabled indirect estimation of canopy height using satellite imagery, particularly time-series data from Sentinel-2. Although Sentinel-2 does not provide height information directly, its multispectral and tem-

poral characteristics allow for canopy structure inference when combined with appropriate machine learning models. Nevertheless, challenges remain in adapting models trained on one geographic region to new areas with different ecological and distributional characteristics. Fine-tuning models with local ALS data is a promising strategy for improving transferability and accuracy. In this study, we leverage dense ALS-derived canopy height maps as ground-truth data to train and validate a deep learning model that predicts canopy height from Sentinel-2 time-series imagery.

This study contributes to the developing deep-learning approaches for canopy height estimation from satellite imagery. It evaluates the performance of Convolutional Long Short-Term Memory (ConvLSTM) based models on three Czech test areas. The models process 40 or more Sentinel-2 (S-2) time-frames spanning an entire year and provide uncertainty estimates using deep ensembles with isotonic regression calibration (Alagialoglou et al., 2022). Ground-truth data for training were sourced from the Bohemian Forest and Switzerland, and fine-tuning was performed on site-specific data.

We aim to provide insights into the reliability of Sentinel-2 derived canopy height maps for local ap-

<footnote n="1">¹Multimedia Understanding Group, Aristotle University of Thessaloniki, Greece</footnote>

<footnote n="2">²Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece</footnote>

<footnote n="3">³Department of Remote Sensing, Global Change Research Institute of the Czech Academy of Sciences, CzechGlobe,
Brno, Czech Republic
lalagial@mug.ee.auth.gr, imanakos@iti.gr, brovkina.o@czechglobe.cz, novotny.j@czechglobe.cz, antelopo@ece.auth.gr</footnote>

<footnote>a ID: https://orcid.org/0000-0002-8361-0589</footnote>

<footnote>b ID: https://orcid.org/0000-0001-6833-294X</footnote>

<footnote>c ID: https://orcid.org/0000-0001-5860-2184</footnote>

<footnote>d ID: https://orcid.org/0009-0004-1017-9160</footnote>

<footnote>e ID: https://orcid.org/0000-0001-8220-8486</footnote>
plications. The objectives of this study are to:

* Assess the performance of fine-tuned ConvLSTM models for estimating canopy height in Czech forests.

* Compare the accuracy of fine-tuned models with their pretrained counterparts.

* Quantify species-specific uncertainties for deciduous and coniferous trees.

# 2 MATERIALS & METHODS

## 2.1 Study Areas

The three test areas are located in the Moravian region of the Czech Republic spanning 3.5 km², 11 km², and 3 km², as shown in figure 1. The three study areas were selected to represent different forest compositions and ecological conditions, allowing us to assess model performance across diverse environments. The forests in Test Areas 1 and 2 are predominantly deciduous, with a small mixture of spruce and pine. Test Area 3 consists mostly of coniferous forest (Fig. 2).

## 2.2 ALS and Sentinel-2 Data

Airborne laser scanning (ALS) data were acquired using the Riegl LMS Q780 airborne full-waveform laser scanner, which was mounted on the Flying Laboratory of Imaging Systems (FLIS) operated by Czech-Globe (https://olc.czechglobe.cz/en/home-en/). ALS data were collected during the years 2023, 2022, and 2024, for the test areas 1, 2, 3 respectively. The acquisition details, including point cloud density and Sentinel-2 availability, are summarized in Table 1. Fine-tuning of the model was conducted on a 1.4 km² subset of Test Area 2, marked in pink color on figure 1. The acquisition details are summarized in Table 1, with some additional information of the available Sentinel-2 imagery of the specific study areas. Despite variations in the point cloud density of the acquired ALS data across the test areas, they are considered dense ground truth maps due to their significantly higher density compared to the interpolated resolution of the smallest Sentinel-2 ground sampling distance (10 m).

The ALS data processing workflow included trajectory calculation, georeferencing, relative orientation of individual flight lines, and export. These steps were conducted using a combination of software tools provided by the scanner manufacturer (POSPac 8.7, RiPROCESS 1.9.2, RiUNITE, GeoSysManager

2.2.4) and the LATools software suite. The resulting laser point clouds were exported and further processed into digital surface models (DSM).

The processed ALS data provide highly accurate canopy height information and serve as dense ground truth references for model training and evaluation. The derived DSM spatial resolution is 0.5 m, ensuring detailed spatial representation of the forest canopy.

A sequence of Sentinel-2 (S-2) Level-1C products, representing top-of-atmosphere reflectance, of the study areas is downloaded for the years of each ALS data acquisition, from the Copernicus Data-pace Ecosystem. The number of available products for each area is given in table 1. Sentinel-2 imagery was collected across all seasons without cloud filtering, ensuring coverage throughout the year. All model inputs and outputs were resampled to a 10 m spatial resolution to match Sentinel-2's highest available resolution.

Tree species classifications were derived from the Forest Management Institute (FMI) dataset for 2022¹.

## 2.3 Model Architecture

The ConvLSTM models combine convolutional layers with long short-term memory (LSTM) networks to effectively process spatial and temporal data. The models were initially trained on Sentinel-2 imagery, using 40 timeframes per year, and calibrated with isotonic regression to produce uncertainty maps. Training data included regions in the Bavarian Forest and forested areas in the whole area of Switzerland. Further details on the training dataset and neural network architecture are available in our previous study (Alaloglou et al., 2022).

The ensemble of models is fine-tuned on a subset of Test Area 2, specifically for the year 2022, as depicted in Figure 1. Fine-tuning employs the Gaussian Negative Log Likelihood loss function, as recommended in our earlier work (Alaloglou et al., 2022). Optimization is performed using the AdamW optimizer with a weight decay regularization coefficient of $1 \times 10^{-3}$ and a learning rate of $1 \times 10^{-4}$, over 100 epochs.

During inference, the model processes 40 uniformly distributed timeframes for a given year without applying any cloud filtering. In practice, more timeframes are utilized by ensembling the results of 150 runs, with each run using a different set of 40 inputs.

<footnote n="1">¹https://geoportal.uhul.cz/mapy/MapyDpz.html</footnote>
<figure><img src="image_180.png" /><figcaption>Figure 1: Overview of three test areas in the Czech Republic, covering 3.6 km², 10 km², and 3 km², respectively. ALS reference data were collected in 2023, 2022, and 2024. Fine-tuning was performed on a 1.4 km² subset of Test Area 2, highlighted in pink. The central coordinates of each test area are: Test Area 1 – (16.833260, 49.004164), Test Area 2 – (16.945512, 48.681688), and Test Area 3 – (16.180660, 49.313461).</figcaption></figure>

<table><caption>Table 1: Overview of parameters for the three study areas, including ALS data acquisition and Sentinel-2 acquisitions.
* The fine-tuning area is a small sub-region within study area 2, as shown in Figure 1.</caption><thead><tr><td>Study Area</td><td>Acquisition Date</td><td>Area [km²]</td><td>Cloud Point Density [points/m²]</td><td>Total Sentinel-2 Scenes</td></tr></thead><tbody><tr><td>1</td><td>28.05.2023</td><td>3.6</td><td>10</td><td>145</td></tr><tr><td>2 *</td><td>13.07.2022</td><td>10.0</td><td>8</td><td>144</td></tr><tr><td>3</td><td>30.04.2024</td><td>3.0</td><td>10</td><td>65</td></tr></tbody></table>

<figure><img src="image_181.png" /><figcaption>Figure 2: Species distribution in the test areas 1-3 and the fine-tune area.</figcaption></figure>

## 2.4 Experimental Design

Two experimental setups were evaluated:

1. **Pretrained Models:** These models were applied without fine-tuning, resulting in higher estimation errors.

2. **Fine-Tuned Models with Dense Ground-Truth:** These models were fine-tuned using dense LiDAR data covering approximately 1.4 km². The fine-tuning area corresponds to a subregion of

Test Area 2, as shown in Figure 1. In terms of species representation, this sub-region follows similar species distribution with the total Test Area 2. However, it is very different to the species distribution of Test Area 3. With this choice we aim to examine the fine-tuned model performance on test areas with both similar as well as different forest type characteristics.

Species-specific uncertainties were computed using tree species maps provided by the Forest Management Institute (FMI) for the reference year 2022. Exploratory statistics of species distributions across the three study areas are illustrated in Figure 2. The species map, originally a vector layer, was rasterized to match the Sentinel-2 resolution with a ground sampling distance of 10 m, using nearest-neighbor interpolation.

The performance of the canopy height models was evaluated using three quantitative metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination ($R^2$).

* MAE: average absolute difference between the predicted and observed values. MAE provides an intuitive measure of overall error, expressed in meters.

* RMSE: square root of the average squared differences between the predicted and observed values. RMSE penalizes larger errors more heavily, making it sensitive to outliers, and is also expressed in meters.
* $R^2$: Represents the proportion of variance in the observed data that is explained by the model. An $R^2$ value close to 1 indicates strong agreement between the predicted and observed values, while values near 0 suggest poor predictive performance.

## 3 RESULTS

Figure 3 presents canopy height estimation results for the three test areas, including absolute error maps and quantitative metrics such as mean absolute error, root mean square error and $R^2$. These figures provide a visual comparison of the errors associated with the pretrained and fine-tuned models across the test areas. Specifically, for Test Area 1, the fine-tuned model achieved a MAE of 1.09m, RMSE of 2.09m, and $R^2$ of 0.88, compared to the pretrained model's MAE of 1.27m, RMSE of 2.24m, and $R^2$ of 0.87. In Test Area 2, the fine-tuned model achieved a MAE of 2.69m, RMSE of 3.63m, and $R^2$ of 0.83, compared to the pretrained model's MAE of 4.45m, RMSE of 5.52m, and $R^2$ of 0.62. Similarly, in Test Area 3, the fine-tuned model achieved a MAE of 1.48m, RMSE of 2.51m, and $R^2$ of 0.66, compared to the pretrained model's MAE of 2.04m, RMSE of 3.18m, and $R^2$ of 0.46. The visual comparison further underscores the improvement, with reduced absolute error maps for fine-tuned models.

Based on the quantitative results and visual analysis shown in Figure 3, the fine-tuned model is overall accurate for all three test areas in different years, comparing to the state-of-the-art (Alagialoglou et al., 2022; Lang et al., 2023; Tolan et al., 2024), with as little as $1.4\text{km}^2$ fine-tuning ground-truth area. Furthermore, the fine-tune model consistently demonstrated superior performance compared to the pretrained model across all test areas and all vegetation types, as evidenced by all three metrics: MAE, RMSE, and $R^2$. This consistent trend across all test areas validates the efficacy of the fine-tuning process in improving model performance.

Similar conclusions are demonstrated in tables 3 and 4. Test Area 2 showed the most significant reductions in the overall MAE and RMSE for the fine-tuned models, particularly for deciduous species. This can be attributed to two factors:

1. The fine-tuning area is a subregion of Test Area 2 for the same year, allowing the models to better adapt to the specific distribution characteristics of the area and time.

2. Test Areas 1 and 3 contain large regions dominated by the "Plantation/Other" class, which gen-

erally exhibits lower canopy height values and, consequently, smaller errors and lower margin for improvement.

Detailed quantitative results for each species across the three test areas are summarized in Tables 3 and 4. These tables demonstrate that the fine-tuned model consistently outperforms the pretrained model across all test areas and species, with significant reductions in both MAE and RMSE. The results show metrics for six species categories, as well as overall metrics for each test area. Species categories include Spruce, Pine, Oak, Other Deciduous, Other Coniferous, and Plantation/Other. The metrics are provided for both pretrained and fine-tuned models to enable direct comparison.

The Oak class, representing approximately 50% of the fine-tuning dataset, dominates the model's learning and exhibits the most consistent accuracy improvements across all test areas with sufficient land cover percentages. For instance, in Test Area 2, the fine-tuned model achieves a MAE of 2.89 m, compared to 5.13 m for the pretrained model. Similarly, in Test Area 1, which corresponds to a different year, the fine-tuned model yields a MAE of 3.23 m, outperforming the pretrained model's MAE of 4.51 m. This trend is also evident in Figure 4. In Test Area 2, the MAE difference between the pretrained and fine-tuned models for shorter oak trees (1–4 meters) exceeds 1.5 times the actual canopy height. In contrast, the "Other deciduous" and "Plantation & Other" classes show MAE improvements closer to 1 times the actual canopy height. Additionally, the fine-tuned model demonstrates consistent accuracy improvements for the class Oak across all canopy height bins, as seen in the left panel of Figure 4. This improvement aligns with the class proportions in the fine-tuning dataset, where Oak dominates (50%), followed by "Plantation & Other" and "Other Deciduous".

Test Area 2, at $10\text{km}^2$, is significantly larger than Test Area 1 ($3.6\text{km}^2$) and Test Area 3 ($3\text{km}^2$). The species distribution in Test Areas 1 and 2 primarily includes deciduous trees, while Test Area 3 is mostly coniferous, excluding the "Plantation/Other" class. For this reason, the analysis focuses on the dominant forest classes, avoiding those with small sample sizes. It is noted that the performance of classes with few pixels, such as the oak in Test Area 3 or the spruce and pine in Test Area 1, should not be taken into consideration. Due to the limited number of pixels available for these classes, results are likely affected by label noise in the species classification map. However, although oak is generally absent in Test Area 3, similarly to spruce and pine in Test Area 1, the results
<figure><img src="image_182.png" /><figcaption>Figure 3: Results for the tree test areas: pretrained and fine-tuned models.</figcaption></figure>
<table><caption>Table 2: Mean and Standard Deviation (std) for ALS measurements and predictions (Pretrained and Fine-tuned), along with number of pixels (n), by Class and Test Area.</caption><thead><tr><th rowspan="2">Class</th><th colspan="4">Test Area 1</th><th colspan="4">Test Area 2</th><th colspan="4">Test Area 3</th></tr><tr><th>ALS mean (std)</th><th>Pretrained mean (std)</th><th>Fine-tuned mean (std)</th><th>n</th><th>ALS mean (std)</th><th>Pretrained mean (std)</th><th>Fine-tuned mean (std)</th><th>n</th><th>ALS mean (std)</th><th>Pretrained mean (std)</th><th>Fine-tuned mean (std)</th><th>n</th></tr></thead><tbody><tr><td>Spruce</td><td>5.73 (5.27)</td><td>5.26 (4.12)</td><td>5.37 (4.60)</td><td>83</td><td>5.16 (5.22)</td><td>6.95 (4.08)</td><td>6.28 (4.66)</td><td>485</td><td>6.28 (4.52)</td><td>9.52 (5.08)</td><td>8.39 (5.12)</td><td>3084</td></tr><tr><td>Pine</td><td>11.48 (3.63)</td><td>9.60 (3.54)</td><td>9.77 (3.59)</td><td>56</td><td>11.53 (3.56)</td><td>13.54 (3.28)</td><td>12.25 (3.16)</td><td>1514</td><td>11.24 (6.10)</td><td>13.69 (4.21)</td><td>13.85 (5.17)</td><td>1567</td></tr><tr><td>Oak</td><td>16.18 (4.61)</td><td>20.59 (3.36)</td><td>18.17 (3.36)</td><td>1541</td><td>15.02 (7.18)</td><td>18.64 (4.58)</td><td>15.80 (6.03)</td><td>32837</td><td>6.26 (2.90)</td><td>15.80 (3.03)</td><td>12.09 (2.80)</td><td>284</td></tr><tr><td>Other Deciduous</td><td>14.96 (6.00)</td><td>17.50 (6.43)</td><td>16.47 (6.40)</td><td>4193</td><td>16.65 (8.09)</td><td>16.14 (5.71)</td><td>17.29 (7.15)</td><td>39060</td><td>6.58 (4.60)</td><td>11.21 (4.89)</td><td>10.08 (5.06)</td><td>1414</td></tr><tr><td>Other Coniferous</td><td>13.03 (5.88)</td><td>15.49 (7.03)</td><td>14.61 (6.79)</td><td>582</td><td>11.30 (7.74)</td><td>11.66 (6.22)</td><td>11.42 (6.89)</td><td>4115</td><td>6.72 (4.56)</td><td>10.36 (4.66)</td><td>9.63 (5.06)</td><td>2215</td></tr><tr><td>Plantation/Other</td><td>0.68 (1.54)</td><td>0.93 (1.22)</td><td>0.75 (1.29)</td><td>29637</td><td>1.98 (3.61)</td><td>4.71 (4.53)</td><td>2.93 (3.86)</td><td>20711</td><td>0.39 (1.33)</td><td>1.49 (2.18)</td><td>1.03 (1.92)</td><td>21913</td></tr><tr><td><strong>Overall</strong></td><td>3.23 (6.15)</td><td>4.59 (7.70)</td><td>4.07 (7.12)</td><td>36092</td><td>12.67 (9.00)</td><td>14.71 (7.51)</td><td>13.36 (8.33)</td><td>98722</td><td>2.35 (4.31)</td><td>4.03 (5.25)</td><td>3.45 (5.12)</td><td>30477</td></tr></tbody></table>

<table><caption>Table 3: MAE and number of pixels (n) for Pretrained and Fine-tuned Models by Class and Test Area.</caption><thead><tr><th rowspan="2">Class</th><th colspan="3">Test Area 1</th><th colspan="3">Test Area 2</th><th colspan="3">Test Area 3</th></tr><tr><th>Pretrained MAE [m]</th><th>Fine-tuned MAE [m]</th><th>n</th><th>Pretrained MAE [m]</th><th>Fine-tuned MAE [m]</th><th>n</th><th>Pretrained MAE [m]</th><th>Fine-tuned MAE [m]</th><th>n</th></tr></thead><tbody><tr><td>Spruce</td><td>1.74</td><td>1.60</td><td>83</td><td>3.33</td><td>2.73</td><td>485</td><td>3.80</td><td>2.76</td><td>3084</td></tr><tr><td>Pine</td><td>2.35</td><td>2.39</td><td>56</td><td>2.91</td><td>2.62</td><td>1514</td><td>3.96</td><td>3.67</td><td>1567</td></tr><tr><td>Oak</td><td>4.51</td><td>3.23</td><td>1541</td><td>5.13</td><td>2.89</td><td>32837</td><td>9.61</td><td>5.90</td><td>284</td></tr><tr><td>Other Deciduous</td><td>3.51</td><td>3.44</td><td>4193</td><td>4.23</td><td>3.23</td><td>39060</td><td>4.93</td><td>3.91</td><td>1414</td></tr><tr><td>Other Coniferous</td><td>3.54</td><td>3.32</td><td>582</td><td>3.73</td><td>3.23</td><td>4115</td><td>4.23</td><td>3.55</td><td>2215</td></tr><tr><td>Plantation/Other</td><td>0.74</td><td>0.60</td><td>29637</td><td>3.17</td><td>1.49</td><td>20711</td><td>1.15</td><td>0.72</td><td>21913</td></tr><tr><td><strong>Overall</strong></td><td>1.27</td><td>1.09</td><td>36092</td><td>4.26</td><td>2.74</td><td>98722</td><td>2.04</td><td>1.48</td><td>30477</td></tr></tbody></table>

<table><caption>Table 4: RMSE and number of pixels (n) for Pretrained and Fine-tuned Models by Class and Test Area.</caption><thead><tr><th rowspan="2">Class</th><th colspan="3">Test Area 1</th><th colspan="3">Test Area 2</th><th colspan="3">Test Area 3</th></tr><tr><th>Pretrained RMSE [m]</th><th>Fine-tuned RMSE [m]</th><th>n</th><th>Pretrained RMSE [m]</th><th>Fine-tuned RMSE [m]</th><th>n</th><th>Pretrained RMSE [m]</th><th>Fine-tuned RMSE [m]</th><th>n</th></tr></thead><tbody><tr><td>Spruce</td><td>2.32</td><td>2.06</td><td>83</td><td>3.97</td><td>3.52</td><td>485</td><td>4.51</td><td>3.47</td><td>3084</td></tr><tr><td>Pine</td><td>2.58</td><td>2.62</td><td>56</td><td>3.81</td><td>3.23</td><td>1514</td><td>4.74</td><td>4.40</td><td>1567</td></tr><tr><td>Oak</td><td>5.57</td><td>4.51</td><td>1541</td><td>6.14</td><td>3.78</td><td>32837</td><td>10.31</td><td>6.42</td><td>284</td></tr><tr><td>Other Deciduous</td><td>4.50</td><td>4.44</td><td>4193</td><td>5.22</td><td>4.16</td><td>39060</td><td>5.83</td><td>4.75</td><td>1414</td></tr><tr><td>Other Coniferous</td><td>4.71</td><td>4.45</td><td>582</td><td>4.70</td><td>4.22</td><td>4115</td><td>5.12</td><td>4.40</td><td>2215</td></tr><tr><td>Plantation/Other</td><td>1.09</td><td>1.02</td><td>29637</td><td>4.28</td><td>2.35</td><td>20711</td><td>1.83</td><td>1.31</td><td>21913</td></tr><tr><td><strong>Overall</strong></td><td>2.24</td><td>2.09</td><td>36092</td><td>5.32</td><td>3.70</td><td>98722</td><td>3.18</td><td>2.51</td><td>30477</td></tr></tbody></table>

<figure><img src="image_183.png" /><figcaption>Figure 4: Left: Difference between the fine-tuned model's MAE and the pretrained model's MAE, normalized by ground-truth canopy height, for each tree species class across bins of ground-truth canopy height. Right: MAE for each tree species class across bins of ground-truth canopy height. Tree species with less than 200 pixels per height bin are not plotted for clarity.</figcaption></figure>

in tables 2, 3 and 4 are included for completeness.

The left part of Figure 4 presents the difference in MAE between the fine-tuned model and the pretrained model, normalized by ground-truth canopy height, for each tree species class across bins of ground-truth canopy height. The higher the normal-

ized ΔMAE, the more significant the improvement achieved by fine-tuning with this specific dataset. On the right side of the figure, the MAE for each species class is displayed across the same bins. In both figures, classes with less than 200 pixels per height bin are not plotted for clarity.
Using the normalized MAE difference shown in Figure 4, we observe that in Test Area 2, the Oak class exhibits the most significant improvement, followed by the "Plantation & Other" and "Other deciduous" classes, which improve similarly. The "Other coniferous" class shows the least improvement. This order corresponds to the class proportions in the fine-tuning dataset: Oak accounts for 50%, "Plantation & Other" for 31%, "Other deciduous" for 14%, and "Other coniferous" for only 4%. Although these results align with expectations based on the dataset composition, one should be cautious, since tree species representation in the fine-tuning dataset is an important factor but not the sole determinant of model performance.

# 4 CONCLUDING REMARKS

The fine-tuned model demonstrates high accuracy across all three test areas in different years, outperforming state-of-the-art approaches with as little as 1.4 km² of fine-tuning ground-truth data. It consistently surpasses the pretrained model in all test areas and vegetation types, with particularly notable accuracy improvements in the Oak class across all canopy height bins, likely due to its dominance in the fine-tuning dataset. Moreover, species showing the most significant improvements correspond to their proportions in the fine-tuning data, reinforcing expectations based on dataset composition. However, caution is needed, as species representation is a crucial factor but not the sole determinant of model performance. This study underscores the importance of evaluating the distribution characteristics of fine-tuning datasets to ensure reliable and localized conclusions.

Recent studies showed that the accuracy of CHMs derived from satellite data varies between deciduous and coniferous forests due to differences in canopy structure, spectral reflectance, and other biophysical properties. For instance, (Alvites et al., 2024) reported that CHM accuracy differed significantly between forest types, with the highest RMSE as a percentage of the mean observed data reaching 17.79% in broadleaf forests and 26.58% in coniferous forests, indicating higher errors in canopy height estimation for coniferous forests. Our research does not offer such insights however offers the perspective of the correlation between the model's performance and the characteristics of the initial training data and the fine-tuning area distribution. This highlights the critical importance of assessing the distribution characteristics of training datasets to draw reliable, localized conclusions.

The "Plantation/Other" class, dominant in Test

Areas 1 and 3, typically shows low errors, leading to smaller improvements for the fine-tuned models (Tables 3 and 4). As shown in Table 2, the mean height values for this class are very low, indicating minimal or no vegetation. Both pretrained and fine-tuned models perform well in these low-vegetation regions. Plantations typically consist of evenly spaced, same-aged trees, leading to a more uniform canopy. This homogeneity simplifies the modeling process, potentially reducing errors (Schwartz et al., 2024).

These findings highlight the effectiveness of fine-tuning in adapting models to site-specific conditions while adding an explainability dimension to uncertainty quantification, particularly in relation to tree species distribution in fine-tuning datasets and target regions. This contributes to improving wide covering canopy height estimation for operational forest inventories. Although this study is a preliminary exploration of species-specific uncertainties, future work will involve experiments with diverse fine-tuning datasets to further evaluate the impact of species-representativeness on the fine-tuning dataset, as well as the effect of specific species on the model's accuracy. Additionally, addressing the challenge of fine-tuning local-scale canopy height models with limited datasets or sparse ground-truth measurements, i.e., field measurements, is crucial for both research and forest industry applications. To tackle this, we plan to explore few-shot learning approaches, such as semi-supervised and active learning techniques.

# ACKNOWLEDGEMENTS

This work was supported by the Ministry of Education, Youth and Sports of CR within the CzeCOS program, grant number LM2023048. Tree species maps were provided by the Forest Management Institute (FMI), a government organization established by the Ministry of Agriculture of the Czech Republic. We also acknowledge the support of the Greek National Infrastructure for Research and Technology (GRNET) for providing AWS Cloud services within the project Copernicus European Forest Foundation Model (CEFFM). Furthermore, we extend our gratitude to the South, Central, and East European Regional Information Network (SCERIN), a constituent member network of the Global Observation of Forest and Land Cover Dynamics program (GOFC-GOLD), for facilitating the emergence of our collaboration, which enabled the success of this study.
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# Machine Learning for Identifying Potential Photovoltaic Installations on Parking Areas

Frederick Kistner* and Sina Keller*

Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology (KIT), Engler Street 7,
D-76131 Karlsruhe, Germany

**Keywords:** Artificial Intelligence, Renewable Energy, Classification, Urban Areas, Sustainable Development, PV Installation.

**Abstract:** Integrating renewable energy systems into urban areas is crucial for sustainable development. This study assesses the potential for installing photovoltaic (PV) systems in parking areas, focusing on a case study region in Hesse, Germany. A machine learning approach is developed to classify parking lots larger than 900 m² into suitable and unsuitable categories. The input data includes OpenStreetMap (OSM), the Authoritative Topographic-Cartographic Information System (ATKIS), and high-resolution geospatial datasets. A reference dataset for the two classification categories is created. Multiple input features are generated, and their significance for the classification task is evaluated. Additionally, several shallow machine learning models are implemented and assessed. The XGBoost model demonstrates the highest accuracy at 99% and is used to classify 10,894 parking areas throughout Hesse. Key suitability features include the Normalized Difference Vegetation Index (NDVI), surface sealing ratios, and vegetation height. The results indicate that approximately 21.8 km² of the parking area is suitable for PV installations, requiring minimal ecological intervention. The methodological approach is scalable for application in other regions, and validation in Frankfurt am Main confirms a strong correlation with solar radiation levels. This study provides a data-driven framework for optimizing urban energy systems and supporting sustainability initiatives.

## 1 INTRODUCTION

Renewable energy technologies are essential to mitigate climate change and ensure energy security (IPCC, 2022). Among these, photovoltaic (PV) systems stand out due to their scalability and adaptability to diverse environments (Santamouris, 2020). The scenarios analyzed by Fraunhofer ISE (Wirth, 2023) estimate that achieving climate neutrality in the German energy sector will require PV capacities ranging from 215 GW to 500 GW, depending on efficiency improvements, public acceptance, and energy system dynamics. In recent decades, solar power costs have decreased significantly, making large-scale plants highly competitive with fossil fuels (Wirth, 2023).

When focusing on urban regions, areas functional for PV installations are lacking. Parking lots are generally seen as underutilized areas or stranded assets, occupying substantial urban land that is mainly nonproductive (Krishnan et al., 2017). Transforming

these areas with solar canopies offers a dual-purpose solution that encourages using renewable energy without requiring additional land resources (Ivanova et al., 2020). Research indicates that PV installations in these areas can substantially boost local energy production and contribute to sustainable urban development (Maier et al., 2024; Marneni et al., 2021; Krishnan et al., 2017).

An important question is how to identify parking lots suitable for installing PV. Current evaluations of parking lot solar installations often depend on generalized assumptions and lack detailed, high-resolution data. This lack can lead to overlooking local constraints and complicating feasibility analyses for PV installations in different parking lot environments. It could also eliminate parking lots, which could be valuable for solar installations.

The proposed study addresses these emerging aspects. While previous studies have explored the feasibility of solar energy installations in urban areas, no existing approach combines high-resolution geospatial data with machine learning (ML)-based classification to identify PV potential in parking areas. This study introduces a novel, automated ML pipeline

<footnote>a https://orcid.org/0009-0002-2601-9538</footnote>

<footnote>b https://orcid.org/0000-0002-7710-5316</footnote>

<footnote>*These authors contributed equally to this work.</footnote>
that leverages OpenStreetMap (OSM), the Author-
itative Topographic-Cartographic Information Sys-
tem (ATKIS), and high-resolution geospatial datasets
to systematically classify parking areas based on suit-
ability for PV installation. We apply advanced pre-
processing and feature engineering techniques to cap-
ture site-specific details. Robust classification al-
gorithms, including the XGBoost, help differentiate
suitable parking lots from unsuitable ones for PV
canopy installations. This methodological approach
goes beyond static, threshold-based filters by using
ML to manage complex, multi-dimensional data.

The main contributions of this study are summa-
rized:

1. **Integration of Heterogeneous Spatial Data:** We unify datasets from OSM, ATKIS, and high-resolution satellite imagery, including normalized difference vegetation index (NDVI), to create a comprehensive feature space containing descriptors for each parking lot.

2. **Automated Preprocessing and Feature Engineering:** We apply automated spatial analysis techniques, such as buffer-based processing, high-resolution land cover segmentation, and geometric feature extraction, to enhance data quality and improve model performance.

3. **Scalable Classification:** We conduct comparisons of supervised machine learning models, which are trained and evaluated on over 1,000 la-beled parking lot examples.

4. **Generic, Transferable Framework:** The methodology is designed to be replicable, allowing for broad applicability in large-scale assessments of PV potential.

5. **Independence from Solar Radiation Models:** A case study illustrates that the classification framework does not rely on solar radiation models, making it especially advantageous for regions without such data.

6. **Actionable Insights:** The analysis provides detailed insights into PV potential by considering sealing rates, vegetation heights, and other parking lot-specific attributes rather than relying solely on simplified metrics such as percentage area reduction.

## 2 RELATED WORK

This section briefly introduces related work on PV
systems in parking lots and their feasibility. First, we
present the facts and benefits of installing PV systems

in parking lots. Second, we summarize the economic
and structural aspects. Lastly, we look at existing spa-
tial approaches to identify and classify suitable park-
ing lots for PV.

**PV Systems in Parking Lots: Generation and Consumption of Energy.** In general, PV systems can generate power close to where it is consumed, thereby minimizing the need for extensive transmission lines. This setup becomes particularly effective when paired with distributed battery storage and other energy converters (Wirth, 2023). Parking lots have become ideal sites for installing decentralized photovoltaic systems. They allow battery storage integration, which helps ease grid congestion during peak production times and reduces the need for long-distance transmission. Consequently, many studies prioritize parking lots for their land use efficiency (Maier et al., 2024; Wirth, 2023; Solar Cluster BW, 2022; Figueiredo et al., 2017).

**PV Systems in Parking Lots: Economical and Structural Aspects.** While rooftop PV solutions are well-established, canopy structures in parking lots involve additional costs for support and retrofitting (Figueiredo et al., 2017). However, future cost reductions and increased incentives for on-site usage, especially when combined with the charging of electric vehicles, are expected to improve the economic viability of such installations (Solar Cluster BW, 2022; Maier et al., 2024). Although structural investments can be approximately 50% higher than those for standard rooftop systems, on-site self-consumption and premium electricity pricing can offset these initial costs (Krishnan et al., 2017; Maier et al., 2024). Indirect benefits include vehicle protection, extended pavement life, and a visible demonstration of commitment to climate action, strengthening public and corporate perception (Solar Cluster BW, 2022).

**Challenges in Identifying Suitable Parking Lots for PV Installation.** Generalized assumptions are often made when focusing on existing studies and approaches to identifying potential parking lots for PV installation. These include, for example, fixed area coverage factors or capacity densities and a lack of high-resolution data on shading, vegetation, or land use constraints (Ludwig et al., 2024; Maier et al., 2024; Krishnan et al., 2017). Besides, parking lots vary widely in ownership structures, functional zones, and site conditions, complicating feasibility analyses for PV installations (Solar Cluster BW, 2022; Maier
et al., 2024). For example, in Germany, several federal states mandate parking PV systems for newly constructed lots exceeding specific size thresholds, influencing scalability and business models, as shown by (Maier et al., 2024; Ludwig et al., 2024). In addition, automated large-scale methods for identifying and classifying suitable existing parking lots remain limited. Consequently, local limitations are often overlooked, leading to inaccurate potential assessments (Wirth, 2023).

# 3 DATASETS

Both input and reference data are essential for creating and evaluating ML approaches to identify potential parking lots for PV installations. We detail the reference and input data in Sections 3.2 and 3.3.

Since ensuring the feasibility and generalization of ML models is crucial, the implementation needs to be performed on a necessarily large actual dataset. Therefore, we train and test the ML models on a representative input-reference dataset that encompasses the parking areas of the German federal state of Hesse. In the subsequent Section 3.1, we give a brief overview of the geographic background of Hesse.

To further validate the ML approach, we investigate solar radiation information of selected parking lots. Section 3.4 summarizes these data.

## 3.1 Study Region

Hesse (German: Hessen) is a federal state bordered by six other states in west-central Germany. Its capital is Wiesbaden, and its largest city is Frankfurt am Main, a significant financial hub. Covering around 21.114 km², Hesse has a population of over six million residents. The landscape features hilly terrain and extensive forests, with about 42% of its land area covered by woodlands. The Rhine River forms the southwestern border, contributing to its varied topography.

To promote sustainability, the Hessian Ministry of Economics, Energy, Transport, Housing and Rural Regions (German: Hessisches Ministerium für Wirtschaft, Energie, Verkehr, Wohnen und ländlichen Raum (HMWEVW)) claims that new parking lots with over 50 spaces must now include solar panels, with an expected 100 new solar-equipped lots each year (HMWEVW, 2023).

Hesse's central location, diverse landscape, and innovative energy policies make it an ideal region for developing our proposed ML models, which focus on potential PV installations in parking areas.

## 3.2 Generation of Reference Parking Lot Data

ATKIS and OSM are the primary data sources for parking lot polygons. One advantage is that these sources are freely available, ensuring the generalization opportunities of the developed approach.

OSM parking areas are identified using tags such as *parking*, *capacity*, *access*, *surface*, *rooftop*. To meet the requirements according to the Hessian Ministry of Economics, Energy, Transport, Housing and Rural Regions (HMWEVW, 2023), the minimum area of the parking lots needs to cover 900 m², approximately 50 spaces. Currently, relevant parking lots that meet the same size criteria are extracted from ATKIS. The OSM and ATKIS parking lots have been merged, and duplicates have been removed. This first step leads to 11,281 parking objects covering a total area of 35.88 km².

For reference data generation, selected parking lots are manually labeled and divided into two categories for PV installations: *suitable* and *unsuitable*. This labeling process is based on high-resolution satellite images for 1,002 parking lots, most of which are randomly chosen. The reference dataset comprises 775 *suitable* and 227 *unsuitable* parking lots. Table 1 visualizes examples for those two classes. Unsuitable parking lots constitute the minority class and are significantly underrepresented in the randomly selected fraction of the dataset, which posed challenges for solving the classification task with ML models. Therefore, additional examples of the minority class were systematically added to the dataset, enabling more robust model training. The resulting reference dataset comprised 22.6% *unsuitable* and 77.4% *suitable* parking lots stored as a geographic layer.

## 3.3 Heterogeneous Input Data Sources

In addition to the generated reference data (see Section 3.2), different input features must be extracted from various data sources.

Since we aim to analyze the potential of installation sites in Hesse, we are built upon a comprehensive dataset comprising various geometrical and environmental factors.

The primary data source includes 11,574 **parking lot objects** described in Section 3.2. These parking lots serve as a foundational basis for identifying suitable locations for PV installations. Almost 9% of these parking lots have labels. All parking lots regarding the **slope** and orientation are investigated. Excluded areas are characterized, for example,
<figure><img src="image_184.png" /><figcaption>Table 1: Exemplary parking lots manually labeled and classified as *suitable* (first row), and *unsuitable* (second row).</figcaption></figure>

by a north slope >5° or a steep slope >30° (see Section 4.1).

Additionally, a raster file at a resolution of 0.2 m × 0.2 m has been generated, providing class predictions at the pixel level from a deep learning segmentation model.<fnref n="1" /> Based on these results, we can calculate ratios within the parking lots and surrounding locations and extract information about the surface texture. In total, the land cover layer consists of eleven classes, such as fully *sealed*, partially *sealed*, tall vegetation, or low vegetation. A raster dataset of NDVI with a spatial resolution of 10 m × 10 m is employed to assess vegetation density and health. NDVI is calculated as the mean during the summer months between 2018 to 2023. Lastly, we include the total *green volume* with a spatial resolution of 100 m × 100 m calculated with an NDVI threshold and a normalized digital surface model.

Based on these input data sources, we generated
several input features, as described in Section 4.

**3.4 Additional Validation Data**

To enhance and validate our proposed approach, we utilize average annual solar radiation data from 193 selected parking lots within the city district of Frankfurt am Main (approximately 20% of Frankfurt's parking lot area). These data have been obtained by manually outlining the polygon shapes of the selected areas based on high-resolution solar radiation information provided by the Hessian solar register (German: Solarkataster Hessen) (Landes Energie Agenkur Hessen, 2025). This online tool is designed to assess

the suitability of rooftops and open spaces for PV installations, considering factors such as solar radiation, shading, and orientation (Landes Energie Agenkur Hessen, 2025).

# 4 METHODS

This section outlines an automated pipeline for
preparing, analyzing, and modeling parking lots to assess the suitability of PV installations.

**4.1 Preprocessing**

Nine buffer zones are established around 10.894 potential parking lots at distances of 0 m, 1 m, 5 m, 7 m, 10 m, 15 m and 20 m to facilitate the parking lots themselves (0 m) and their surrounding environment analysis. These buffers are dissolved with different input sources such as land cover, NDVI, and green volume (see Sections 3.3 and 4.3), and intermediate layers are generated for each buffer distance.

## 4.2 Splitting in Training and Test Dataset

Based on the labeled reference data, we split the dataset using stratified sampling with a ratio of 80 : 20 into a training and test set using scikit-learn (Pedregosa et al., 2011). In addition, we apply a fixed random seed to ensure reproducibility. The imbalance of the dataset is the main reason for the stratification. In pre-testing, we have systematically added labeled data points from the minority class to the dataset. This manual extension effectively implements an upsampling approach with actual data, addressing the dataset imbalance (see Section 3.2) (More, 2016).

The test set consists of parking lots the models have never encountered. It is used exclusively for final evaluation and has never been part of the training phase. As outlined below, the 80% portion designated for training is split into three cross-validation folds for hyperparameter optimization.

Figure 1 shows the distribution of unsuitable and suitable parking lots in the training and test sets.

**4.3 Feature Extraction**

Based on different input data sources, we extract additional features.

To extract the features, the calculated buffer zones
overlap with the thematic layer NDVI, green volume,

<footnote n="1">¹This deep learning segmentation model was a result of
the extended research within the project.</footnote>
<figure><img src="image_185.png" /><figcaption>Figure 1: Class distribution of the training and test set. The unsuitable parking lots are visualized in red, while the suitable parking lots are green. The respective darker colors represent the test subset.</figcaption></figure>

and land cover information. Therefore, we calculate **five key geo-statistics features**: minimum, maximum, mean, median, and sum for each buffer area and the NDVI and green volume.

Regarding the high-resolution land cover, we extract **three geostatistical features per buffer zone**: count, area, and proportion for the eleven land cover classes. The count is given in pixel numbers, while proportion represents the ratio between the occurring land cover classes.

With every overlapping and calculation, intermediate layers are created and merged into one layer via spatial join, and new attributes are assigned for each buffer distance and topic.

In a subsequent feature extraction step, we compute additional geometry-related features for each parking lot object. These include bounding box dimensions, e.g., *length*, *width*, *elongation*, *perimeter*, *compactness*, *convexity ratio*, *centroid coordinates*, and *solidity*. The **bounding-box area-to-lot area ratio** captures the object's fit within its bounding box, while the shape area and perimeter are updated accordingly. Furthermore, **three additional class ratio features** are generated, relating sealed (fully or partially sealed) pixel counts to vegetation counts (low, medium, or tall).

Finally, all existing NDVI and green volume features are normalized by each lot's area, producing respective variants divided by shape area and further enhancing the comparability of metrics across differently sized parking lots.

After applying correlation analysis, we discard highly correlated features with a correlation coefficient >95%, resulting in 70 final input features. Additionally, we have tested dimension reduction techniques without considerable impact on the models' performance.

In the final step, we remove 77 parking lots with missing values due to district boundaries exceeding

limits. We then combine the input features with the suitability class labels of the reference dataset (1,002 objects), which contain information about whether the dataset will be used for training or testing. The remaining 9,892 parking lots are not labeled and will be applied in the final model.

Within the Frankfurt city district, we combine the solar radiation data (see Section 3.4) with the labeled parking lots to evaluate the model's predicted suitability classes and our manually labeled reference class later.

## 4.4 Model Development and Optimization

Several ML methods exist for supervised learning. We study selected state-of-the-art **shallow learning ML** approaches to solve the classification task: Random Forest (Breiman, 2001), XGBoost (Chen and Guestrin, 2016), Extra Trees (Geurts et al., 2006), LightGBM (Ke et al., 2017), and CatBoost (Dorogush et al., 2018). All of the models are tree-based ensemble approaches.

Each model is initialized with baseline configurations (e.g., random state, default parameters) and evaluated on identical training and test sets to ensure comparison regarding the results.

As **hyperparameter tuning**, we rely on the Bayesian optimization strategy (BayesSearchCV from `scikit-optimize`) (Frazier, 2018; Head et al., 2018). The hyperparameters `max_depth`, `n_estimators`, `learning_rate`, and `class weighting` are tuned within predefined ranges using three-fold cross-validation. To address class imbalance, *balanced accuracy* is used as the primary optimization metric, ensuring equal consideration of minority class performance. After identifying each model's hyperparameters, the final models are trained on the entire training dataset and evaluated on the test set.

Besides, we use SHapley Additive exPlanations (SHAP) (Lundberg and Lee, 2017) to explain and clarify the **feature importance** of the input data. SHAP measures each feature's contribution to model predictions, providing a unified and theoretically sound approach to interpreting machine learning models (Lundberg and Lee, 2017).

This analysis improves our understanding of the features that influence the classification of PV suitability.
# 5 RESULTS AND DISCUSSION

The results of the applied ML models are evaluated based on commonly applied classification metrics. We rely on Precision, Recall, F1 Score, Log Loss, Balanced Accuracy (BA), ROC-Auc, and Average Accuracy (AA). Given the dataset's imbalance, BA and F1 scores are prioritized to ensure robust performance across all classes. Log Loss also evaluates the reliability of probability estimates, while ROC-AUC and Average Precision provide insights into the models' discriminative abilities.

Table 2 summarizes the classification results of the ML models on the test dataset. All ML models demonstrate strong performance with high Precision, Recall, and F1 scores.

In particular, the **XGBoost** model achieves the highest balanced accuracy with 99% on the test dataset, along with exceptional precision, Recall, and F1 score values. XGBoost exhibits the lowest log loss with 0.05, indicating excellent calibration and reliable probability estimates.

The CatBoost and Extra Trees classifiers also perform robustly, with balanced accuracy scores of 99% and log loss values of 0.05 and 0.06, respectively. These models maintain high precision and recall, accurately classifying suitable and unsuitable parking lots.

Although the Random Forest and LightGBM models show slightly higher log loss values of 0.10 and 0.12, respectively, they still deliver commendable balanced accuracy scores of 99%. Their strong performance underscores their effectiveness in handling class imbalance within the dataset.

Slight Recall and F1 score variations emphasize nuanced differences in each model's ability to capture relevant positive instances while maintaining overall accuracy (see Table 2).

Due to the consistent classification performance of the XGBoost classifier in terms of balanced accuracy, this model is selected for further analysis and classification of all parking lots in Hesse. Unlike other models, XGBoost efficiently handles imbalanced datasets, reducing bias toward the majority class (suitable parking lots) while maintaining high recall for the minority class (unsuitable lots). Due to its computational efficiency and handling of high-dimensional feature spaces, it is well-suited for large-scale applications, making it a practical choice for real-world urban energy planning. By confirming that XGBoost outperforms other tree-based ensemble models, we provide strong empirical justification for its selection as the optimal classifier for this study.

Figure 2 visualizes the classification results of the

<figure><img src="image_186.png" /><figcaption>Figure 2: Confusion matrix of the XGBoost model on the (a) training dataset and (b) test dataset.</figcaption></figure>

<figure><img src="image_187.png" /><figcaption>Figure 3: SHAP plot illustrating the feature importance for the XGBoost model (five most important features, listed left) based on the training dataset. Colors indicate feature values. While high values are red, low values are blue.</figcaption></figure>

XGBoost model of the training and test datasets, respectively. It elucidates the number of correctly and incorrectly classified areas and the influence of various features on the model's predictions.

Figure 3 illustrates how individual input features influence the XGBoost model's classification of parking lots as suitable or unsuitable for PV installations. Each SHAP value represents the importance of a feature, with blue indicating low and red indicating high feature values. The most significant feature is the **NDVI Mean (0m)**, representing the mean vegetation index within the 0 m buffer (location of the parking lots themselves). High NDVI values (red), indicating dense vegetation, reduce the suitability for PV installations.

Another essential feature is the proportion of **tall vegetation** on the parking lot, where higher proportions (red) also decrease PV suitability. In contrast, the **fully sealed count** indicates that areas with more sealed surfaces (blue) increase suitability, as these surfaces are better suited for PV. Additional features such as the **lot perimeter** and the **NDVI sum/lot** ratio within the 2 m buffer zone show similar trends, where sealed and less vegetated areas enhance suitability.

In summary, vegetation-related features negatively impact PV suitability, while sealed surfaces contribute positively.
<table><caption>Table 2: Classification results of the applied ML models on the test dataset. BA means Balanced Accuracy. The best-performing model is highlighted.</caption><thead><tr><th>ML Model</th><th>Precision</th><th>Recall</th><th>F1 Score</th><th>Log Loss</th><th>BA</th></tr></thead><tbody><tr><td>Random Forest</td><td>1.00</td><td>0.96</td><td>0.98</td><td>0.10</td><td>0.98</td></tr><tr><td><strong>XGBoost</strong></td><td><strong>1.00</strong></td><td><strong>0.98</strong></td><td><strong>0.99</strong></td><td><strong>0.05</strong></td><td><strong>0.99</strong></td></tr><tr><td>Extra Trees</td><td>1.00</td><td>0.98</td><td>0.99</td><td>0.06</td><td>0.99</td></tr><tr><td>LightGBM</td><td>1.00</td><td>0.98</td><td>0.99</td><td>0.12</td><td>0.99</td></tr><tr><td>CatBoost</td><td>1.00</td><td>0.97</td><td>0.99</td><td>0.05</td><td>0.99</td></tr></tbody></table>

# 6 APPLICATION OF THE BEST MODEL

Since we aim to investigate all parking lots within Hesse, we apply the strong classification model, XG-Boost, to the entire parking lot data.

## 6.1 Assessing the Parking Lot Dataset of the Study Region Suitable for PV

**Analysis of Parking Lots Suitable for PV in Hesse:**
The parking lot dataset's overlap with settlement areas provides insight into the distribution of parking lots in urban and rural regions. A weighted area is calculated for each class to quantify classification uncertainty, shown as black error bars in Figure 4. This weighted area is derived by multiplying the actual area of each parking lot by the model's prediction probability for that class label. Weighted areas are summed separately for parking lots within and outside settlement areas. Their deviations are used to compute the standard deviation of the weighted areas.

The total potential amounts to 28.541 km² of PV suitable parking lots, with a model prediction uncertainty of 0.47 km². These parking lots represent approximately 0.1% of Hesse's total area. This analysis highlights the potential for using parking lots for PV installations. However, this potential is gradually reduced when practical constraints, including technical feasibility, economic viability, legal considerations, and environmental impact, are considered.

Identifying the percentage of parking lot areas suitable for PV systems is essential to accurately and generally assess the potential of PV parking lot installations. This area coverage factor, which indicates the percentage, can vary significantly between studies, ranging from 18% to 79.4%. These differences are attributed to the varying assumptions and methodologies used in the existing investigation (Maier et al., 2024).

Of the 28.541 km² classified as suitable, 1.606 km² are identified as existing rooftops. Although rooftops are also suitable for PV, they are

excluded from the analysis. Additionally, 1.637 km² of tall vegetation and 0.401 km² of medium-sized vegetation were subtracted, as removal of vegetation would have a significant environmental impact. Regarding these exclusions, the upper range of available parking lot area for PV is 24.921 km².

To minimize environmental impact, PV installations should focus on paved surfaces, reducing the available area to 21.804 km² (= 50.8% of the theoretical potential parking lot area). These figures align with previous upper estimates for parking PV potential.

By incorporating various spatial and geometric features as input for the data-driven models, we can provide explainable information on what the ML approaches rely on to solve the classification task. This *explainability* can be a first step towards more transparency, which is often lacking according to (Maier et al., 2024), concerning the overall topic of PV on parking lots.

<figure><img src="image_188.png" /><figcaption>Figure 4: Distribution of parking lots in urban and rural areas on the reference dataset (dark red and green) and the entire dataset of Hesse (light red and green), classified using the XGBoost model. Shaded areas (right) represent parking lots outside urban areas. The black boxplots represent error bars, while the estimated area represents the theoretical potential of suitable PV parking lots.</figcaption></figure>
## 6.2 Solar Energy Investigation of Selected Parking Lots

One key challenge in validating ML models for PV suitability assessment is the limited availability of high-resolution solar radiation data. In the case of Hesse, such data is not freely accessible for the entire region, restricting our ability to perform a comprehensive solar energy potential analysis.

To address this challenge, we have conducted a case study using 193 manually validated parking lots in Frankfurt am Main, where high-resolution solar data from the Hessian Solar Register was available. This targeted validation serves as a representative test of our model's effectiveness.

Figure 5 compares solar radiation (kWh m⁻²) for 193 parking lots in Frankfurt am Main, classified as suitable or unsuitable by the model. A Mann-Whitney U test confirms that parking lots predicted as suitable exhibit significantly higher solar radiation than those predicted as unsuitable (p<0.001, Cliff's Δ= -0.848). Suitable lots show median solar radiation of 950 kWh m⁻², compared to 702 kWh m⁻² for unsuitable lots. These findings demonstrate that the model effectively identifies locations with higher solar potential despite having no solar radiation as an input. Further, this result shows that the manual labeling of selected parking lots as suitable and unsuitable has been conducted correctly.

While the analysis covers approximately 20% of all parking lots in Frankfurt am Main, the results validate the suitability predictions regarding solar radiation. The classification correlates well with expected solar potential, even though our model does not explicitly use solar radiation as an input feature.

Integrating this framework with high-resolution solar radiation datasets and an economic analysis tool, such as the Hessian solar register, could optimize yield and return-on-investment analyses for PV deployment across the entire state of Hesse without requiring manual labeling.

These findings demonstrate that our feature-driven classification approach can effectively predict solar suitability without comprehensive solar radiation datasets. Future work could integrate solar modeling techniques or partner with governmental agencies to obtain broader access to radiation data for full-scale validation.

<figure><img src="image_189.png" /><figcaption>Figure 5: Violin plots comparing solar radiation for unsuitable (red) and suitable (green) parking lots classified by the XGBoost model.</figcaption></figure>

## 7 CONCLUSIONS AND OUTLOOK

In conclusion, the innovative framework proposed establishes a solid foundation for assessing the PV potential in urban parking areas at scale, encouraging new research and applications in renewable energy and urban development. By integrating diverse datasets, such as OSM, ATKIS, and high-resolution geospatial imagery and utilizing advanced machine learning techniques, we identified and classified suitable parking areas for PV canopy installations. The model's effectiveness is illustrated through its application in Hesse, Germany. The XGBoost model achieved an impressive classification accuracy of 99%, distinguishing suitable sites based on features like vegetation indices and sealing ratios. About 21.8 km² of parking areas were identified as suitable for PV, promoting sustainable energy solutions.

Validation against solar radiation data further confirms the model's reliability without requiring explicit radiation inputs. This approach bridges the gap between theoretical potential and actionable insights, equipping urban planners and policymakers to optimize energy systems toward climate-neutrality goals. Its independence from high-resolution radiation models increases its applicability in data-scarce environments, and the automated processing enhances scalability.

Future research directions can include:

* Assess Economic Feasibility: Analyzing installation costs and return-on-investment to help prioritize PV deployment sites.

* Integrate Additional Validation Datasets: Collaborating with regional agencies for broader solar data access to evaluate classification performance.

Overall, this study offers a scalable, data-driven framework for assessing PV potential in urban set-
tings. This has implications for urban planning, re-
newable energy investments, and policy-making. The
method can be replicated in other regions, validating
its applicability across diverse geographical and cli-
matic contexts.

ACKNOWLEDGEMENTS

We thank the HMWVW for the part-funding of this
work.

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Remote Sensing-Based Temporal Analysis of Aletsch Glacier Retreat
(1990–2020)

Andrija Krtalić¹ IDª, Ana Kuveždić Divjak¹ IDᵇ and Kristina Zeman Šteković²

¹University of Zagreb Faculty of Geodesy, Kačićeva 26, Zagreb, Croatia

²SOPRA PROJEKT LTD, Remetinečka cesta 5C, Zagreb, Croatia

{andrija.krtalic, ana.kuvezdic.divjak}@geof.unizg.hr, kristina.zmn@gmail.com

**Keywords:** Glacier, Remote Sensing, Climate Changes, Sentinel 2, Landsat 5.

**Abstract:** Glaciers represent an important component of the cryosphere and are among the most sensitive indicators of climate change and global warming. Over recent decades, climate change has significantly accelerated glacier retreat, prompting the development of various monitoring methods, such as the Glacier Monitoring in Switzerland (GLAMOS) program. Key parameters for understanding glacier dynamics include changes in mass balance, length, surface area, and snow accumulation, all of which are closely tied to climatic variations. These changes manifest as alterations in glacier morphology and mass, resulting in notable retreat compared to previous decades when such trends were less pronounced. This study focuses on analysing changes in the Great Aletsch Glacier, the largest glacier in the Alps, over the period 1990–2020 using remote sensing data/techniques. Automated glacier detection and mapping methods were applied, utilizing optical satellite data from Sentinel-2 and Landsat-5 missions. Glacier extents were delineated and analysed over the 30-year period, integrating satellite-derived estimates with official GLAMOS data and climatological records from the MeteoSwiss agency. The results reveal a reduction of approximately 5.2% in the surface area of the Great Aletsch Glacier, providing valuable insights into the glacier’s response to ongoing climate change.

# 1 INTRODUCTION

Glaciers are massive, permanent ice bodies formed over centuries through the accumulation and compaction of snow into dense ice masses, constituting a critical component of the cryosphere (Hock and Truffer, 2024, Haeberli et al., 2007). While most glaciers are concentrated in polar regions, they are also found in temperate and tropical latitudes at higher altitudes, where they play a crucial role in sustaining human activities. Glaciers in these regions act as vital freshwater reservoirs, support agricultural practices, and often serve as significant attractions for tourism.

On a global scale, glaciers are widely recognized as one of the most visible and sensitive indicators of climate change (Hock and Truffer, 2024); Winsvold et al., 2016). This is particularly evident in alpine and tropical glaciers, which are especially vulnerable to climatic fluctuations. These glaciers are critical for local water resources and human activities, yet they

have experienced significant retreat over recent decades due to global warming. To address this, numerous glacier monitoring programs have been established, including Glacier Monitoring in Switzerland (GLAMOS, 2025). Key glacier parameters, such as changes in mass, length, area, and snow cover, are directly linked to climatic variations, with glacier length often responding to climate change after a time lag (Nie et al., 2024, Pandey and Venkataraman, 2012).

Given the importance of glaciers and the potential hazards associated with their retreat, international monitoring initiatives have been developed. Programs such as the World Glacier Monitoring Service (WGMS), Global Land Ice Measurements from Space (GLIMS), the Randolph Glacier Inventory (RGI), GLAMOS, and the Global Terrestrial Network for Glaciers (GTN-G) provide critical insights into the status and dynamics of glaciers worldwide.

<footnote>ªID https://orcid.org/0000-0002-9441-0179</footnote>

<footnote>ᵇID https://orcid.org/0000-0003-1059-8395</footnote>
Monitoring glaciers poses significant challenges due to their vast scale and the logistical difficulty of collecting in situ data. Consequently, remote sensing has become an essential tool for observing glacier dynamics and detecting changes over time. Remote sensing data/technologies enable the systematic collection of Earth observation data, facilitating the monitoring of temporal and spatial changes in glaciers while also aiding in hazard assessments. The literature identifies four primary remote sensing methodologies for glacier monitoring, categorized based on the type of data used: (1) optical and near-infrared sensor data, (2) thermal infrared sensor data, (3) microwave electromagnetic spectrum data, and (4) synthetic aperture radar (SAR) interferometry (Wen and Wang, 2024).

This study focuses on the use of optical and near-infrared sensor data, specifically from the Landsat 5 satellite system, to analyse surface changes in the Great Aletsch Glacier. By leveraging these methods, the research aims to provide valuable insights into the glacier's temporal dynamics and its response to climatic changes over the last three decades.

## 2 REMOTE SENSING APPROACHES TO GLACIER MONITORING AND HAZARD ASSESSMENT

Mountain glaciers in tropical and temperate regions are very good indicators of climate change (Burns and Nolin, 2014) and are key resources for drinking water, maintaining agriculture and hydropower, especially in dry seasons. The melting and retreat of glaciers due to global warming leads to the creation and enlargement of glacial lakes, which can pose a significant threat in the form of glacial lake outburst floods (GLOF). Globally, about 15 million people are directly exposed to the impacts of potential GLOFs (Taylor, Robinson, Dunning, 2023), and the most endangered areas are Central Asia (India, Pakistan, China), the Andes (Peru), and the European Alpine countries (Switzerland, Austria, Italy), (Emmer et al., 2022).

The continued loss of glacier ice and the expansion of glacial lakes due to climate change, particularly in temperate and tropical densely populated areas, represent a global natural hazard that requires attention to minimize loss of life and destruction of infrastructure. Accordingly, the need to monitor glacier changes has arisen, and global, regional and national monitoring programs of

particular importance have been established. The above topic is mostly dealt with by Indian and Pakistani scientists with a focus on Himalayan glacial phenomena (Nie et al., 2024, Pandey and Venkataraman, 2016, Shafique et al., 2018), European scientist with a focus on Alps glacials (D'Agatha et al., 2018), and American scientists with a focus on Andean and North American geospace (Bolch and Wheate 2010, Romanov and Gutman, 2000).

Remote sensing-based observations of glacial landforms have proven to be crucial for monitoring changes, as routine in-situ data collection in isolated, hard-to-reach mountainous regions is often very difficult due to large spatial coverage and weather variations (Nie et al., 2024, Wang et al., 2025). There are significant advantages of remote sensing for crisis management, such as continuous observations before and after a certain event, the possibility of integrating a wide range of different complementary sensors and easy integration into geoinformation systems, the use of adaptable and simple algorithms, a significant reduction in fieldwork, archiving and reuse of images. Potential disadvantages, some of which are being addressed and reduced over time, include insufficient spatial and temporal resolution, data incompatibility, technical complexity, the high cost of access to commercial images (sub-meter spatial resolution), and the limited use of optical sensors depending on weather conditions (Kerle and Oppenheimer, 2002).

The first satellite systems for remote sensing had a spatial resolution of 80 m and a small number of spectral channels (Landsat 2). The launch of the Landsat 4 and 5 satellite systems with the Thematic Mapper sensor (Landsat TM), with better spatial and spectral resolution, enabled improved mapping and monitoring of glacial areas. In addition to these data, multisensor satellite data such as MODIS (Rigs et al., 2006), ASTER (Wang et al., 2025), the upcoming Landsat 7 and 8 missions and the new TM (Masek et al., 2006, Shafique et al., 2018), Sentinel 2 (Frank et al., 2016) and also radar images like Sentinel-1. Satellite images are then integrated with global or national meteorological data, digital elevation model (DEM) data, and in situ data using various modelling techniques (Ashraf et al., 2016).

During research, various multispectral channels are most often combined in order to distinguish the desired land cover classes by classification (Dozier, 1989, Rosenthal and Dozier, 1996), useful indices such as the Normalized Difference Vegetation Index (Rouse et al., 1974) and the Normalized Difference Snow Index (Hall, Riggs, 2011) are calculated. and today they are increasingly using machine and deep
learning methods (Chu et al., 2022). Many scientists are engaged in measuring various parameters of snow and ice such as the thickness and movement of glaciers using LIDAR (Deems et al., 2013) to estimate the melting of glaciers, or the thickness and volume of snow and ice through aerial photographs (Meyer et al., 2022), and to assess drinking water as well as potential hazards.

The research presented in this paper aims to map and analyse the spatial and temporal changes of the largest Alpine glacier, the Great Aletsch, over a 30-year period (1990–2020). This analysis exclusively utilizes open-access datasets from the Landsat 5 and Sentinel-2 satellite systems, employing selected remote sensing methodologies to provide insights into glacial dynamics and associated hazards.

# 3 MATHERIALS AND METHODS

## 3.1 Study Area

The object of the study is the Great Aletsch Glacier, located in the Swiss Alps and representing the largest Alpine ice mass with an area of 78.49 km² and a volume of approximately 12 km³ (data from 2017). The Great Aletsch is one of 1400 Swiss glaciers located in the Alps, and extends between 1650 and 4160 meters above sea level, with a pronounced retreat of the lowest point (GLAMOS, 2025, GLAMOS 1880-2021). The glacier consists of three main tributaries – *Aletschfirn, Jungfraufirn and Ewigschneefeld* (Figure 1) which merge at the *Konkordiaplatz* with an ice thickness of 800 - 1000 meters and form a common 'tongue', which extends approximately 15 km to the southwest (GLAMOS).

According to official data (GLAMOS 1880-2021), the glacier area has decreased from 86.62 km² in 1976 to 78.48 km² (decrease of 9.4%) in 2016. The Great Aletsch Glacier was at its peak around 1860. At that time, it was about 3 km longer than today, and the glacier edge was about 200 m higher in the Aletsch Forest area, and this area stands out today as a belt of relatively young vegetation in the landscape (UNESCO, 2025).

Continuous monitoring of the length of the Great Aletsch Glacier has been taking place since 1870, and a reduction of -3459 meters in length has been recorded in the period 1870-2021. More detailed monitoring was established in 1918 with the installation of the first measuring rod at 3350 m above sea level at Jungfraufirn. The data are publicly available on the GLAMOS website (URL4). Since 1990 (which was chosen as the reference year in this

study), the length of the glacier has decreased by - 1315.4 m (GLAMOS 2022).

The Greater Aletsch consists of two distinct areas. The first is the accumulation area at higher altitudes, where the annual increase in fresh snow during the winter months is greater than the snowmelt in the summer. The second area is the ablation area below Konkordiaplatz, where the melting of ice is stronger due to higher summer temperatures, and glacier mass is lost in the lower part. It is assumed that this area will see the largest changes in the results of remote sensing data/methods.

<figure><img src="image_190.png" /><figcaption>Figure 1: Appearance and position of the main components (Aletschfirn, Jungfraufirn and Ewigschneefeld) of the Great Aletsch Glacier in the Swiss Alps (Source: GLAMOS 1880-2021).</figcaption></figure>

## 3.2 Climatic Factors Affecting the Great Aletsch Glacier

The development and maintenance of glaciers are largely determined by climatological, orographic and hydrographic features. The average annual temperatures in the upper part of the Great Aletsch Glacier are below freezing, while in the glacier tongue area they are slightly above 0°C (Figure 2a). Another very important indicator for glaciers is the amount of precipitation. Figure 2b shows the relatively dry climate in the glacier tongue, but the highest precipitation in all of Switzerland is measured
in the Jungfraujoch glacier accumulation area, which reaches an average of about 3000 mm per year, influenced by precipitation from the north.

<figure><img src="image_191.png" /><figcaption>Figure 2: a) Average annual temperature in °C and b) average annual precipitation in mm in Switzerland in the period 1991-2020 (MeteoSwiss, 2025). The location of the Aletsch Glacier is marked with a yellow ellipse.</figcaption></figure>

The amount of snow cover is another important factor for glacier maintenance. Official data from MeteoSwiss show a statistically significant decline in the number of days with fresh snow, as well as in annual fresh snow depths, since the 1960s in many areas of Switzerland, which is accelerating glacier melting (MeteoSwiss, 2025).

Under the influence primarily of temperature and precipitation, the glacier is divided into an area of accumulation (the upper part of the glacier) and ablation, i.e. erosion/landing (the tongue of the glacier), which will later be important for the analysis of the results of this research.

The Jungfraujoch station has been active since 1933 and is located at 3571 m above sea level on the top of the glacier, i.e. in the accumulation area. The climate temperature average for the period 1991-2020 is from -13.3°C in the coldest month of the year (February) to 0.6°C in the warmest month (August). From the annual temperature average monitored until the 1930s, it is evident that the average annual temperature is increasing slightly.

Insolation has been increasing since 1980, and some of the sunniest years occurred after 2000. This development is less pronounced in areas with higher altitudes, namely alpine areas.

## 3.3 Input Data and Methods

All data and software used for the realization of this research are publicly available and free of charge. QGIS, version 3.28, with the Semi-Automatic Classification (SCP) plugin was used as the primary software for the realization of the work.

### 3.3.1 Landsat 5 and Sentinel-2 Data

This research is based on the use of images from the Landsat 5 (launched in 1984) and Sentinel-2 (launched in 2015). The study used only images from the Landsat 5 satellite system, in order to use data from the same sensor for the entire time period. Exceptionally, images from the Sentinel-2 satellite system (launched in 2015) were used due to better spatial resolution. The period for which Landsat 5 data is available is 1984-2012, and for Sentinel-2 from 2015 to 2020. Landsat 5 carries the passive sensors Multi-Spectral Scanner (MSS) and Thematic Mapper (TM) and orbits at an altitude of approximately 705 km with a temporal resolution of 16 days. The TM sensor data were used, whose spatial resolution per multispectral channel is 30 m. Sentinel-2A and Sentinel-2B (launched in 2017) are equipped with optical-electronic multispectral sensors (MSI) that create a set of 13 spectral channels (image): 4 channels with a spatial resolution of 10 m, 6 channels with a spatial resolution of 20 m and 3 channels with a spatial resolution of 60 m. Sentinel-2A and Sentinel-2B provide a temporal resolution of 5 days, and a radiometric resolution of 12 bits. The images are available in L1C and L2A processing modes, with the L2A product being fully atmospherically corrected, and the images with this level of processing were used in this work.

For both systems, L2/L2A preprocessing level images were downloaded in GEOtiff format. Satellite systems and dates when the images were created:

- Landsat 5 - 18/08/1990, Scene cloud cover: 4%,

- Landsat 5 - 20/08/2000, Scene cloud cover: 11%,

- Landsat 5 - 09/08/2010, Scene cloud cover: 12%

- Sentinel-2 -07/08/2020, Scene cloud cover: 0.5%
### 3.3.2 Glamos Data

Glacier Monitoring in Switzerland (GLAMOS) is a glacier monitoring program in Switzerland that systematically documents and monitors long-term changes in glaciers in the Swiss Alps. GLAMOS is jointly managed by ETH Zurich and the Universities of Freiburg and Zurich and the CC Cryospheric Commission. The GLAMOS database contains basic data and facts on more than 1,400 Swiss glaciers covering an area of 641 km², as well as inventories/databases on changes in glacier length, mass, volume, etc. The inventories contain representations of the outer edges, surfaces and debris covers of all Swiss glaciers.

For the purposes of this work, data on inventories from 1976 (Glacier Inventory, 1976), 2010 (Glacier Inventory, 2010) and 2016 (Glacier Inventory, 2016) and 2022 (GLAMOS, 2022) were downloaded in order to create a mask. With glacier boundaries for cutting individual rasters and for accuracy control. The downloaded data were used in the QGIS software as a shapefile, and were further processed.

The GLAMOS Glacier Inventory data for the years 1976, 2010, 2016 and 2022 were downloaded in a form suitable for processing in QGIS software, i.e. as shapefiles with associated formats (shp, shx, qpj, prj, dbf, cpg).

### 3.3.3 Copernicus DEM

Digital Elevation Model (DEM) used as a basis for display and analysis of results. The DEM (DEM GLO-30 Public, 2021) was downloaded from the Copernicus Global Digital Elevation Model platform through the OpenTopography portal. The required area is downloaded in the form of GeoTIFF format with 30 m spatial resolution. Through the QGIS software, a hypsometric display with shading and color scale was created for a realistic display.

### 3.3.4 Input Data Transformation

The downloaded data had different reference systems and datums (Table 1). Therefore, it was necessary to transform all input data into a single project geodetic datum and coordinate system so that the data could be combined. GLAMOS databases store data in the system used in Switzerland and Liechtenstein, while DEM raster data, as well as satellite images, were downloaded in global systems. Finally, the EPSG code 3857 Pseudo Mercator system, which is most commonly used for applications such as Google Maps, OpenStreetMap, ArcGIS, ESRI, was chosen

for the research so that the analysis results could be verified with real images widely available to the public (Google maps, etc.).

<table><caption>Table 1: Reference systems of input data in research.</caption><thead><tr><th>Down loaded data</th><th>GLAM OS 1976, 2010, 2016</th><th>Coper nicus DEM</th><th>Landsat 5 Sentinel-2</th><th>Data analysis</th></tr></thead><tbody><tr><td>EPSG code</td><td>2056</td><td>4326</td><td>32632</td><td>3857</td></tr><tr><td>CRS</td><td>CH1903 +</td><td>WGS8 4 GPS</td><td>WGS 84 / UTM 32N</td><td>WGS 84 Pseudo-Mercator</td></tr></tbody></table>

Further processing of all raster and vector data was performed with EPSG code 3857. Due to their size and the slowness of the processing process, the satellite images were cropped to the area of interest using the SCP tool (Clip option), and Landsat 5 images were resampled to Sentinel-2 spatial resolution. Originally, the Landsat 5 and Sentinel 2 images were in the EPSG 32632 reference system, i.e. WGS84, UTM zone 32N, and all images were cropped to a smaller area, where the glacier is visible.

For further mapping and analysis, a glacier mask was created using the GLAMOS database, with the assumption that publicly available data is of the highest accuracy in demarcating individual glaciers from the Great Aletsch 3b).

### 3.3.5 Normalized Difference Snow Index

The Normalized Difference Snow Index (NDSI) has been shown in the literature to be a reliable algorithm for mapping glaciers due to the fresh snow that covers most of the surface of each glacier (1). Reflectivity varies by snow and ice category - freshly fallen snow has very high reflectivity in the visible and infrared regions. Firn (granular one-year-old snow) has 25-30% lower reflectivity than fresh snow, and glacial ice has high reflectivity in the blue (0.4-0.5 µm) and green (0.5-0.6 µm) wavelength bands that drop sharply to almost zero in the red (0.6-0.7 µm) band. Debris and debris on the glacier surface significantly reduce reflectivity (Pandey et. al., 2016). This algorithm is commonly used in snow/ice mapping applications as well as glacier monitoring. Pixels with a value greater than 0.0 are considered to contain snow cover (Riggs et al., 2016).

NDSI is calculated according to the formulas:

$$NDSI = \frac{\text{Green-SWIR}}{\text{Green+SWIR}} \quad (1)$$
### 3.3.6 The Normalized Difference Vegetation Index

The normalized difference vegetation index (NDVI) was created in order to later classify the land cover more easily. NDVI (2) is the most used indicator for detecting changes in vegetation cover. NDVI is calculated according to the formulas:

$$NDVI = NIR - RED / NIR + RED \quad (2)$$

### 3.3.7 Supervised Land Cover Classification

Glacier mapping was conducted through supervised land cover classification of the broader Great Aletsch Glacier region. This analysis utilized satellite imagery from the Landsat 5 system for the years 1990, 2000, and 2010, as well as Sentinel-2 imagery for 2020. Supervised classification, a digital image processing technique, was employed to distinguish land cover types based on their spectral characteristics. The primary goal was to produce a thematic land cover map, specifically isolating the glacier surface from the surrounding rocky terrain and vegetated areas.

For each analysed year, one set of rasters was created and a training set of samples for classification was created. The more "homogeneous" areas of the image belonging to the same land cover class were selected, according to the judgment and experience of the operator. The selection of ROI polygons is facilitated by the application of the NDSI and NDVI indices for spotting and extracting snow and vegetation areas. RGB images created with combinations of channels 5-4-3 for Landsat 5, and 11-7-4 for Sentinel 2 were also used. Such combinations of channels qualitatively distinguish snow from vegetation and the surrounding rocky area.

Based on the selected samples for 1990, land cover classification was performed using three methods: Minimum Distance, Maximum Likelihood (classical approaches), and Random Forest (a machine learning method), implemented through the specialized SCP plugin, for the purpose of examining the most appropriate method. After multiple attempts, the Minimum Distance and Maximum Likelihood methods produced unrealistic results and were therefore excluded from further analysis.

Random Forest (Hansch, 2025) is a classification based on machine learning. More samples result in a longer, but better-quality process because there are more samples on which it "learns" how to create a decision. Patterns are created exclusively by manually drawing polygons for compatibility with SNAP and SCP tools. Random Forest is a special machine learning technique based on iterative and

random creation of a decision tree, i.e. a set of rules and conditions that define a class.

First, the input features are defined, in this case spectral bands by selecting samples of individual classes. Random Forest calculates several decision trees based on the following parameters: (1) number of training samples: the number of pixels from the training sample that are randomly used to train the model; 5000 was chosen according to the instructions from the SPC, and (2) number of trees: the number of decision trees; the higher the number of trees, the higher the accuracy of the model. 100 was chosen. The separation of trees is defined according to the principle of the Gini coefficient. A pixel is classified as, for example, class 1 if the majority of decision trees evaluated it as class 1.

The causes were selected along the glacier to represent the entire class of “glaciers”, i.e. different types of glaciers cover such as fresh snow, granular snow and glacial ice. The class contains a very wide range of values because glaciers consist of uneven deposits of different types of snow, ice, debris i.e. moraine material and the like. The class of “rocks” has a uniform, relatively low reflectance across all spectral channels and is marked with a light brown colour. The class of “vegetation” has an increased reflectance around 4.0 µm. The class of “shadows” is separated because without it the rock and glacier classes cannot be properly classified.

# 4 RESULTS AND ANALYSIS

## 4.1 NDSI and NDVI

The NDSI was calculated for all weather data sets, and the one for 1990 served as a reference value for comparisons. By analysing the image, it is possible to conclude that the higher pixel values (darker colour) are in the upper, accumulation part of the Great Aletsch Glacier, which has more precipitation, i.e. where snow is retained throughout the year. The lower part of the glacier has lower pixel values (weaker colour) where the snow cover is thinner 3).

Only values above 0.0 are selected and shown in the image. Several “lines” running from Konkordiaplatz along the entire length of the glacier tongue can also be successfully observed, these are moraines – accumulations of sediments of unconsolidated glacial debris, regolith and rocks (debris), sometimes called glacial till. The moraine is distinguished by its appearance and remote sensing methods, but it is an integral part of the glacier.
By analysing the NDVI for 1990, it can be seen
that in the area of interest, rock, sand and snow are
present to a large extent (values -0.1 - 0.1). Only
surfaces with values higher than 0.4 (low vegetation
such as mountain grasslands) are highlighted on this
display.

4.2 **Glacier Mapping**

Before analysing the results, only the glacier of
interest, the Great Aletsch, was separated from the
edges of other, surrounding glaciers. In this way, the
analysis focused only on the body of the Great
Aletsch glacier, which was the goal of this research.
All rasters (NDVIs and classification results using the
*Random Forest* method for each year of observation
1990, 2000, 2010 and 2020) were extracted using the
mask shown in Figure 6b (in QGIS using the
*Extraction raster by mask layer* option). This resulted
in multiple polygons (glacier area for each observed
year) that were merged into a single multipolygon.
Using Field calculator, a new attribute called Area
(km²) was added and the area of the polygon
representing the glacier for each observed year was
calculated using the expression "area($geometry)/
1000000". For the sake of a more realistic display of
details, smoothing of the outline of the glacier was
done because the outer line of the glacier remained
square by converting the raster into a vector form.
Through the tool Processing Toolbox - QGIS
geoalgorithms - Vector Geometry Tools, Smooth
geometry was created for each layer for the observed
years 1990, 2000, 2010 and 2020.

The final data sets for analysis are represented by
4 vector layers – polygons of the mapped Great
Aletsch Glacier in selected years for each of the
methods used.

4.3 Results Analysis

The analysis involved a comparison of the mapped
glacier area obtained by individual methods, and
verifiable real field information and officially
available data. The analysis of the mapped areas of
the Great Aletsch Glacier obtained by the NDSI
calculation methods (Figure 3) and the Random
Forest classification method (Figure 4) indicates
certain changes in the glacier area in the observed
period, i.e. the retreat of the Great Aletsch Glacier in
selected years was detected and documented.

According to the results of the NDSI with a set of
rasters from: 1990, 2000, 2010 and 2020, a regular
glacier retreat is visible, which represents the melting
of glaciers in accordance with climate change. There

is very little change in the accumulation area, while
the pronounced retreat of the glacial mass is most
pronounced in the glacier tongue. This can also be
well monitored in the cuts of the accumulation area
(glacier fans). The glacier surface, especially
pronounced in the glacier tongue, was the largest in
1990 and has been continuously decreasing over the
30-year period (Figure 3). The glacier retreat is most
pronounced longitudinally and in accordance with the
orographic characteristics of the terrain (following
the glacier valley).

<figure><img src="image_192.png" /><figcaption>Figure 3. A single view of the all NDSI for the years: 1990, 2000, 2010 and 2020 (left). Enlarged parts of the glacier surface to better see differences by year (right: 'fan' - top right, 'tongue' - bottom right).</figcaption></figure>

The results of the Random Forest classification
method (Figure 4) show that the areas in all observed
years are smaller and narrower than the glacier areas
obtained by calculating the NDSI. Also, the notches
in the accumulation area of the glacier (fan) are larger
and more pronounced, and the glacier tongue is
visibly narrower. The image details mainly show the
three observed years, while the glacier surface layer
for 1990 barely appears. Namely, the layers are
complex in time/chronology and logic dictates that
the reduction in the glacier area is followed from the
initial observed year when the glacier is largest to the
following years when melting is increased and the
area is smaller.

However, according to the results of the Random
Forest classification, the glacier area for 1990 is
smaller than its area for the following two observed
years. The irregularity may arise due to insufficient
education of the operator performing the
classification, inadequate samples for classification,
spatial resolution of the satellite image and other
technical reasons. It should also be noted that the
<figure><img src="image_193.png" /><figcaption>Figure 4. A single view of the all classification results of Random Forest method for the years: 1990, 2000, 2010 and 2020 (left). Enlarged parts of the glacier surface to better see differences by year (right: 'fan' - top right, 'tongue' - bottom right).</figcaption></figure>

classifications for 1990, 2000 and 2010 were carried out with Landsat 5 images with a spatial resolution of 30 m, and for 2020 with Sentinel-2 images with a spatial resolution of 10 m. However, this fact did not affect the calculation of the NDSI.

Still, these irregularities did not affect the spatial patterns and the glacier retreat was still pronounced during the observed years in accordance with the orographic characteristics of the terrain. The retreat can also be monitored on the cuts of the accumulation area (fan). By comparing the results of the two mapping methods, it is possible to see that the classification according to the Random Forest method shows much more pronounced glacier moraines with debris material, i.e. the classification does not

systematize these parts as a glacier class but as a non-glacial surface, which further glacier retreat in the observed years. The above reduces the accuracy of mapping glacier surfaces because the moraine is an integral part of the glacier, but consists of accumulations of sediments of unconsolidated glacial debris, regolith and rocks (debris). Several of the previously mentioned reasons reduce the accuracy of the classification method for correctly mapping the Great Aletsch Glacier and lead to the conclusion that the Random Forest method is quite subjective and subject to greater limitations in correctly mapping glaciers than the NDSI calculation method. The area of the Great Aletsch Glacier for 2010, according to the Random Forest classification method, is 76,583 km2, which is a deviation of -1.98% compared to the official GLAMOS data for the same year (78,131 km², Table 2).

<table><caption>Table 2: Reference systems of input data in research.</caption><thead><tr><th>Year</th><th>Area (km2) NDSI</th><th>Area (km2) Random Forest</th><th>Area (km2) GLAMOS</th></tr></thead><tbody><tr><td>1976</td><td></td><td></td><td>86,628</td></tr><tr><td>1990</td><td>83.155</td><td>73.157</td><td></td></tr><tr><td>2000</td><td>83.583</td><td>75.623</td><td></td></tr><tr><td>2010</td><td>81.766</td><td>76.583</td><td>78,131</td></tr><tr><td>2016</td><td></td><td></td><td>78,488</td></tr><tr><td>2020</td><td>78.801</td><td>70.459</td><td></td></tr></tbody></table>

A comparison of the implemented glacier mapping methods showed that, in all observed years, the glacier area mapped using the NDSI calculation was significantly larger than that mapped using the Random Forest classification method (Figure 5, Table 2). The largest difference in glacier areas was also observed for 1990, in accordance with the aforementioned irregularity for 1990.

<figure><img src="image_194.png" /><figcaption>Figure 5. Comparison of the areas of the Great Aletsch Glacier obtained by the NDSI methods (blue tones) and Random Forest classification (green tones) by survey years (a) 1990, (b), 2000, (c) 2010 and (d) 2020.</figcaption></figure>
Table 2 lists the areas of the Great Aletsch Glacier (in km²) calculated using the NDSI and Random Forest classification methods, as well as the areas of the glaciers from the GLAMOS database for 1976, 2010 and 2016 in order to make the results comparable since reference in situ information could not be collected.

Comparison with actual data from the GLAMOS database for approximately the same period shows that, in general, the NDSI index method provides more reliable and consistent data. In all observed years, the glacier area is larger through the NDSI index compared to the actual data, although the reference mapping boundary was selected as the NDSI value ≥ 0.1 based on viewing satellite images Timelapse Google Earth Engine. The area of the Great Aletsch Glacier for 2010 using the NDSI index method is 81,766 km², which is a deviation of 4.65% compared to the official GLAMOS data for the same year (78,131 km²).

The survey did not include the collection of reference field data; therefore, the reference boundary of the mapping was determined arbitrarily and a value of NDSI ≥ 0.1 was selected based on the review of Google Earth Engine satellite images. The assumption is that reference data from the field would contribute to raising the reference limit around NDSI ≥ 0.4, and thus the mapped area of the glacier would be smaller and more consistent with GLAMOS official data.

The analysis determined that the mapping of the Great Aletsch Glacier was more reliable and of higher quality based on the calculation of NDSI with a reference value of NDSI ≥ 0.1. Therefore, these results were linked to the DEM Copernicus GLO-30 digital relief model (30m) to produce a new vector layer and marked hypsometric elevations showing the lowest recorded points of the mapped glacier in the observed years. In 1990, the lowest point of the Great Aletsch Glacier was at approximately 1637 m above sea level, in 2000 at approximately 1670 m, in 2010 at 1735 m above sea level, and in 2020 the retreat of the glacier mass reached 1798 m above sea level (Figure 6).

# 5 CONCLUSIONS

The analysis demonstrated that glacier mapping using the automated calculation of the NDSI index yielded more accurate and reliable results than the Random Forest classification method. While both methods showed some deviations from the GLAMOS reference data—NDSI slightly overestimating glacier areas and Random Forest slightly underestimating them—the NDSI method produced spatial patterns

<figure><img src="image_195.png" /><figcaption>Figure 6. The retreat (melting) of the Great Aletsch Glacier mapped using the NDSI method, with the lowest recorded elevations.</figcaption></figure>

that were more consistent with the orographic characteristics of the terrain. This method allowed for better visualization of glacier retreat, particularly along the tongue of the glacier and in the accumulation area, with fewer mapping errors observed on the surface of the Great Aletsch Glacier.

According to relevant literature, the precise determination of a reference NDSI threshold can significantly enhance the accuracy of glacier surface mapping. In this study, the reference threshold was set to NDSI ≥ 0.1, informed by literature and validated using Google Earth Engine Timelapse imagery. For 2010, the NDSI method estimated the area of the Great Aletsch Glacier at 81.766 km², deviating by 4.65% from the official GLAMOS data (78.131 km²). Conversely, the Random Forest method showed a smaller deviation of -1.98% for the same year. However, visual analysis revealed that the Random Forest method produced more significant mapping errors, while the NDSI approach provided more coherent and reliable results.

The findings suggest that combining these methods (data fusion) has the potential to further improve the accuracy of glacier mapping and monitoring. The NDSI index, in particular, proved to be a simple yet effective methodology for glacier mapping, especially when the reference threshold is verified using reliable in situ data. This study highlights the utility of using free satellite data and