bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Impact of the Türkiye February 2023 Earthquakes on Cultural Heritage Sites: A Multi-Disciplinary Approach Utilizing ARIA Maps and Social Media Collaboration
Authors: Demir Nusret , Çilingiroğlu Çiler
Published: May 2024

Abstract. The earthquakes that occurred in our country on February 6, 2023, significantly damaged and destroyed cultural and historical assets. The Habib-i Nejjar Mosque in Antakya and other important historical sites suffered significant damage. This study provides a comprehensive account of the post-earthquake efforts carried out by volunteers from various disciplines, including archaeology, cartography, space sciences, and architecture, using social media platforms. The volunteers created an extensive database for cultural and archaeological sites in all 11 provinces that were declared disaster zones. This database made it easier to conduct a thorough evaluation of the damage. The database was augmented with surface displacement data, enabling an analysis of damage levels with the exactitude facilitated by the gathered information. The utilization of the Advanced Rapid Imaging and Analysis (ARIA) maps was pivotal in this study. The maps, created through partnerships between NASA and different space agencies, offer intricate satellite imagery and analysis of surface displacement. This is essential for evaluating the effects of earthquakes on cultural heritage sites. The utilization of ARIA maps in this study facilitated an accurate assessment of the seismic impact on cultural and historical resources. Additionally, Sentinel 1 images are used to generate displacement maps with the use of the LICSAR tool and the SBAS method. The study examined the state of about 1500 cultural heritage sites in 11 provinces in the aftermath of the earthquake. The report incorporated data from media coverage and input from relevant parties present at the scene, providing a thorough assessment of the situation following the disaster. For instance, the research revealed substantial shifts at prominent locations, like the UNESCO World Heritage site of Arslantepe in Malatya. This site experienced a displacement of over one meter towards the south and 75 cm towards the west, along with a subsidence of approximately 10 cm in the surrounding area. Furthermore, the study presented a visual representation that depicted the quantity of impaired cultural heritage sites in every province, providing a comprehensive evaluation of the impacted cultural and archaeological resources. This comprehensive strategy not only emphasized the magnitude of harm to cultural heritage but also emphasized the significance of interdisciplinary cooperation in disaster response and heritage preservation.
Standard PSInSAR approach: Experimental study on the sensitivity of different bounds change on the deformation
Authors: Ewais Mostafa , Balz Timo
Published: May 2024

Abstract. An experimental study conducted on standard PSInSAR technique to check the sensitivity of different bounds on the deformation results. This study is motivated by the results obtained from employment of different parameter ranges, with the assumption of the linear behavior of the deformation. despite implementation of slight parameter change shows similarity in most of the PS points. However, significant PS points display slightly different deformation measurements and trends. This change in the PS points motivated an experimental study to evaluate, analyze and assess the results from each different bounds through searching the solution space to find the best fitting to the estimated model, besides assessing of the temporal coherence estimator as quality and fitting indicator. A high-resolution TerraSAR-X (TSX) dataset is used to estimate the deformation using linear model and check the behavior over time. These dataset cover Wuhan city as a case study within two years to avoid non-linearity in the deformation. The main part of this experiment is the descriptive of the statistics across all trials of different bounds. Then, assessing the measurement results of the trials, and the coherence estimator that used as a quality indicator to assess the quality of the PS points in comparison with the standard deviation. The results of the measurements indicate that while the coherence estimator select the PS points based on their stability and fitting to estimated model but does not provide a big difference as function of different bounds through searching the solution space. The results are confirmed with the available global Positioning system (GPS) station in our study area and displays similar trends and patterns over all the trials with slight differences with the different parameters range. The statistical analysis and assessment reveals differences in the velocity results and erroneous during changing the parameters range.
Influence of 3D models choice on cervical outline measurements of teeth
Authors: Gaboutchian Armen , Knyaz Vladimir , Maximov Anatoly , Zheltov Sergey
Published: May 2024

Abstract. Among different study techniques, which are used in dental research, measurements of teeth play one of the most important roles. However classical measurement techniques usually show consistent results on morphologically complete objects, while teeth can be found in different conditions, depending, for instance, on the natural wear degree. In order to increase the sample, which is especially important when findings are not abundant, and to improve the analytical part, alternative techniques have been proposed, among which cervical measurements are considered to be informative, especially taking into consideration morphological and methodological importance of the cervical area. Algorithms of automated digital measurement techniques also use the cervical area for providing stability of results. Visualisation of the tooth cervical margin and reconstruction of its projections can be achieved by two conventional imaging techniques, which are tomography (preferably high-resolution) and intra-oral confocal optical scanning. They both were used for obtaining 3D reconstruction of upper premolar taken from palaeoanthropological materials. In line with applying the same automated coordinate system setting algorithm to both types of reconstructions, contours of their enamel cervical margins were defined and their projections to horizontal plane were obtained and measured. Despite the fact that 3D reconstructions from different imaging sources technically can serve for running automated odontometry, measurement results, especially in comparative studies, should be handled with attention.
South-to-South Cooperation in Multi-Source Satellite Data for Improving Food Security
Authors: Balz Timo , Gao Ruonan , Musakwa Walter , Magidi James , Shao Zhenfeng
Published: May 2024

Abstract. This study aims to highlight the importance of south-to-south cooperation in remote sensing and present preliminary results from our joint project between China and South Africa, focusing on multi-sensor remote sensing data for food security. Scientometric analysis was used to demonstrate growing research output and cooperation among countries in the Global South. Furthermore, the first results of our crop classification methods were presented on a test site in the Sanjiang Plain, China. The preliminary crop classification results from this test site showed promising accuracy. South-to-south cooperation in remote sensing has the potential to address shared challenges and promote sustainable development through exchange of knowledge and resources. The ongoing joint project between China and South Africa demonstrates the benefits of such collaboration in developing robust remote sensing techniques for improved food security monitoring and decision making in the Global South.
A Deep Neural Network for Road Extraction with the Capability to Remove Foreign Objects with Similar Spectra
Authors: He Haiqing , Wei Yan , Zhou Fuyang , Zhang Hai
Published: May 2024

Abstract. Existing road extraction methods based on deep learning often struggle with distinguishing ground objects that share similar spectral information, such as roads and buildings. Consequently, this study proposes a dual encoder-decoder deep neural network to address road extraction in complex backgrounds. In the feature extraction stage, the first encoder-decoder designed for extracting road features. The second encoder-decoder utilized for extracting building features. During the feature fusion stage, road features and building features are integrated using a subtraction method. The resultant road features, constrained by building features, enhance the preservation of accurate road feature information. Within the feature fusion stage, road feature maps and building feature maps designated for fusion are input into the convolutional block attention module. This step aims to amplify the features of different channels and extract key information from diverse spatial positions. Subsequently, feature fusion is executed using the element-by-element subtraction method. The outcome is road features constrained by building features, thus preserving more precise road feature information. Experimental results demonstrate that the model successfully learns both road and building features concurrently. It effectively distinguishes between easily confused roads and buildings with similar spectral information, ultimately enhancing the accuracy of road extraction.
Deep-FG-DSM: Fine-Grained DSM for High-resolution UAV Imagery Based on Deep Implicit Occupancy Networks
Authors: Hou Yongmao , Zhang Haibo , Wang Xin , Zhan Zongqian
Published: May 2024

Abstract. Nowadays, DSM (Digital Surface Model) is one of most important products that has been widely applied in digital city or smart city. Over the last decades, the common way is to follow the conventional photogrammetric pipeline for generating DSM, which is often not efficient and yields noise with small geometric details lost. Inspired by the development of deep implicit occupancy network, in this paper, we presented a learning-based method for obtaining fine-grained DSM, i.e., fine Deep-FG-DSM. In particular, high-resolution UAV imagery together with the corresponding original point cloud are employed to improve DSMs preserving higher details, two heads that embed the features of images and 3D points are deployed, and a MLP (Muti-layer Perceptron) is appended to decode these embedding into continuous occupancy probabilities for predicting the existence (or not) of surface point. Our experimental results demonstrate the robustness of our model against both sparse and noisy point clouds. While generating DSMs, it retains high-frequency details from high-resolution UAV images while maintaining relatively high accuracy. For point cloud obtained after simplification with average sampling resolution of d=5m, the MAE (Mean Absolute Residual Error) is 2.15m.
Integrating Data from Terrestrial Laser Scanning and Unmanned Aerial Vehicle with LiDAR for BIM Developing
Authors: Blaszczak-Bak Wioleta , Masiero Andrea , Bąk Paweł , Kuderko Kamil
Published: May 2024

Abstract. The use of Building Information Modeling (BIM) in building construction and management is becoming increasingly common. Nevertheless, the generation of BIM models for already existing buildings is still an operation requiring a significant human effort. The generation of a geometrically reliable and complete BIM model requires geometric information on all the building parts. Since acquiring such information with a unique acquisition tool is quite hard, integration of data acquired with different acquisition tools and platforms is strongly recommended in order to obtain a geometrically complete 3D description of the building. This work presents a procedure for integrating data acquired with Terrestrial Laser Scanning (TLS), UAV (Unmanned Aerial Vehicle) LiDAR (Light Detection and Ranging) and Smartphone with LiDAR, showing the obtained results on two case studies, two buildings in the campus of the University of Warmia and Mazury in Olsztyn. Finally, a BIM model have been successfully generated in both the case studies by using the Blender software.
Three-dimensional forest structure features extraction based on TomoSAR
Authors: Hu Zhentao , Peng Xing , Xie Qinghua , Huang Tingting , Wu Yulan
Published: May 2024

Abstract. Forest structure is an important indicator for evaluating forest ecosystem and an indispensable input parameter for estimating forest carbon sink. Synthetic Aperture Radar (SAR) Tomography (TomoSAR), as a novel microwave remote sensing technique with 3D imaging capability, has been demonstrated to be an valuable tool for accurate inversion of forest structure. At present, the forest vertical structure parameters inversion is almost all concentrated on the forest height estimation. However, the spatial structure of forests is complex, and it is not enough to only use the forest height to express the forest structure. This fails to identify tree canopy, branch layer and underlying vegetation layer, etc., resulting in the partial and inaccurate forest vertical structure information. To solve this problem, this paper constructs 3D structural indices from tomograms, namely Horizontal Structure Index (HS) and Vertical Structure Index (VS). The HS primarily describes canopy density, compensating for the lack of information on the horizontal dimension of forest structure provided by traditional TomoSAR techniques; while VS describes the complexity of canopy distribution in the altitude direction, further enriching information on forest structure vertically. In order to verify their feasibility and validity, HS and VS are extracted using six fully-polarized L-band SAR images covering the Krycklan watershed in Sweden. Moreover, the accuracy is verified by using external high-precision LiDAR data. The results indicate a strong correlation between the structural indices extracted from TomoSAR and those extracted from LiDAR.
Orthorectification of Bridges from High Resolution Satellite Images
Authors: Kim Seunghee , Kim Taejung
Published: May 2024

Abstract. High-resolution satellite imagery requires high-resolution height reference data to create orthorectified images. High-precision digital surface data and LiDAR data are expensive to acquire quickly and accurately. Facility databases, on the other hand, are very efficient because once established, the information can be utilized persistently. The temporal resolution between the structure in the database and the captured image affects the ortho resolution created. Landmarks such as bridges are rarely subject to changes after construction. This makes facility database a suitable material to generate orthophotos of bridge structures. In this paper, we used a bridge database to perform orthorectification for structures with height variations. We removed relief displacement from satellite imagery at different angles and verified orthorectification feasibility for bridges of different heights and lengths. The orthoimage was overlaid with a topographic map to verify the orthorectification visually. As a result, we confirmed that the facility database was able to generate orthophotos of the bridges. The length and slope of bridge structures indicated the need for precise database updates in the future research.
Multi-sensor Data Analysis for Aerial Image Semantic Segmentation and Vectorization
Authors: Knyaz Vladimir A. , Kniaz Vladimir V. , Zheltov Sergey Yu. , Petrov Kirill S.
Published: May 2024

Abstract. One of the urgent and constantly in demand problems is updating maps. Maps, representing geo-information in vector form, have undoubted advantages in compactness and ”readability” compared to aerial photographs. The issue of maps actuality is critically important for rational urban planning, precision farming, the relevance of the cadastre and other geospatial applications. Various sources of data are used for maps updating, with aerial imagery being the main and rich source of information. Automatic processing of aerial photographs makes it possible to efficiently extract vector information, providing operational monitoring and accounting for changes that have appeared. The presented study addresses the problem of multi sensor information fusion in order to obtain accurate vector information. We use aerial images as a main data source and additionally the data of laser scanning and ground survey to increase performance of automatic image semantic segmentation and vectorization. The proposed framework is demonstrated on the task of forest monitoring.
Accurate Segmentation Method for Roadside Lampposts Based on Vehicle-Mounted Lidar Point Clouds
Authors: Li Dengfeng , Li Yaoyu , Fan Yuhang , Tang Shengjun
Published: May 2024

Abstract. The extraction of roadside lampposts constitutes a significant research focus within the domain of vehicular LiDAR point cloud object retrieval. Addressing the complexities inherent in discerning lampposts amidst convoluted vegetation in diverse roadway settings, this study introduces an innovative, progressive technique for the individualized extraction of lampposts utilizing vehicular LiDAR point clouds. The proposed method initiates with a bifurcation of the primary point cloud into terrestrial and aerial subsets via the Cloth Simulation Filter (CSF) algorithm. Subsequent processes involve the extraction of distinct lamppost structures from aerial point clouds through a methodology integrating elevation-normalized spatial partitioning and directional coverage analysis, thereby facilitating precise lamppost localization. The culmination of this process involves the refinement of lamppost point clouds through supervoxel clustering complemented by a non-discretization filter grounded in Principal Component Analysis (PCA). The efficacy of this novel approach is substantiated through empirical studies employing a LiDAR dataset encompassing extensive adhesive scenarios, whereupon comparative analysis with extant methodologies reveals its enhanced proficiency in isolating individual lampposts in complex environments.
Research on Satellite and Ground Multi-Sensor Collaborative Sensing Method for Floating Plants
Authors: Li Naiyi , Xin Liang , Meng Chen
Published: May 2024

Abstract. Dynamic monitoring of water environment is the basis of maintaining urban security and promoting urban sustainable development. To effectively solve the problems caused by the large-scale flooding of floating aquatic invasive plants, such as water environment destruction, clogging river, water quality pollution, water ecological balance destruction, etc., a satellite and ground multi-sensor collaborative sensing method for floating plants is proposed in this paper. For the monitoring of floating plants in macro watershed, the vegetation index and chlorophyll concentration were used to extract floating plants, analyzing, and calculating the distribution and coverage area of floating plants based on 4-band high-resolution satellite images. For the monitoring of floating plants in small watershed, combined with visible light video, multispectral images, and LiDAR data, optimized spectral feature variable processing, semantic segmentation network, and symmetric function neural network are utilized to obtain real-time information on the invasion location, distribution pattern, and coverage area of floating plants. In this paper, the upstream basin of the Huangpu River in Shanghai in 2023 was selected as the experimental area. The distribution of floating plants in different watercourse and different periods was successfully obtained by this method, and the coverage area was accurately calculated with an identification rate of more than 90%, which provided technical support for cross-regional management and efficient cleaning of floating plants.
Research on Monitoring and Application of Ecological Restoration Engineering in Open Pit Backfilling Mines Based on Satellite Remote Sensing Data
Authors: Liang Shuneng , Li Yang , Zhang Ning , Xiao Chenchao , Wei Hongyan
Published: May 2024

Abstract. The restoration and management of mining ecological environment is an important component of China's ecological civilization construction. The implementation of restoration projects is to avoid the serious impact caused by the continuous deterioration of mining ecological structure, and is a key way to maintain regional ecological security and energy and mineral security. Therefore, efficient and rapid monitoring and evaluation of the effectiveness of mining ecological restoration is an essential and important link. With the rapid development of remote sensing technology, remote sensing monitoring has become an important means to objectively, quickly, and accurately obtaining changes in the mining environment. It can provide timely and long-term monitoring of mining and backfilling conditions in mining areas. This article uses the multi temporal stereo images of the Resource 3 satellite over four years to monitor the mining and backfilling situation of the Zhungeer open-pit coal mine area in Inner Mongolia. By reconstructing the Digital Surface Model (DSM) of the open-pit mining area, the changes and thresholds of the multi temporal DSM data are statistically analyzed to extract the mining and backfilling areas, and the earthwork volume is calculated. The results showed that from 2013 to 2016, the main large-scale mining faces in the study area of Zhungeer open-pit mining area had a total mining operation of about 563.5 million cubic meters, and a total backfilling operation of about 604.29 million cubic meters; During the period from 2016 to 2018, the main large-scale mining faces in the region had a total of approximately 721.71 million cubic meters of mining operations and a total of approximately 805.42 million cubic meters of backfilling operations. The overall operational intensity increased from 2016 to 2018. The research results show that based on satellite image data, it is convenient and efficient to obtain DSM and corresponding changes of multiple time periods in mining areas. Monitoring results can provide important support for regional environmental governance and protection, as well as safety production in mining areas.
Towards Sustainable Urban Energy: A Robust Deep Learning Framework for Solar Potential Estimation
Authors: Lin Weiyan , Zhu Jiasong , Hua Yuansheng , Li Qingyu , Mou Lichao , Zhu Xiao Xiang
Published: May 2024

Abstract. Rooftop photovoltaic is considered as a cost-effective and environmentally friendly solution to energy challenges in urban areas. To ensure photovoltaic efficiency, it is essential to accurately estimate rooftop solar potential and deploy solar panels wisely. During the past few years, deep learning-based estimation methods have emerged and mainly rely on inferring rooftop orientations from aerial imagery. However, we note that rooftops often appear diversely when images are taken at different solar azimuths, and this can lead to orientation misclassification. To address this, we propose a robust solar potential estimation framework, mainly composed of a rooftop orientation prediction network and a bilateral solar potential estimation module. Specifically, we first classify rooftops into five orientations, i.e., east, west, south, north towards, and flat with a semantic segmentation network. Afterward, opposing orientations are merged to alleviate misclassification caused by variant data acquisition time. Eventually, we compute solar potentials based on PVGIS and a weighting scheme. Experimental results on the RID dataset demonstrate the effectiveness of our approach in improving the accuracy of solar energy estimation.
On the RFI Detection in Differential Interferometric Synthetic Aperture Radar
Authors: Liu Yanyang , Tao Mingliang , Li Jieshuang , Li Tao , Chen Junli
Published: May 2024

Abstract. Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites.
Overview and Benchmark on Multi-Modal Lidar Point Cloud Registration for Forest Applications
Authors: Campos Mariana Batista , Castanheiro Leticia Ferrari , Shah Dipal , Wang Yunsheng , Kukko Antero , Puttonen Eetu
Published: May 2024

Abstract. Light Detection and Ranging (LIDAR) is widely acknowledged as a robust tool for monitoring forest structure, dynamics, and changes. To achieve a high-complete forest structural model, LiDAR data acquisition from both aerial (above-canopy) and terrestrial (below-canopy) platforms is commonplace. Consequently, in such multi-modal LiDAR cases, robust data registration is required for accurate forest analysis, such as biomass and canopy growth. Yet, multi-modal LiDAR registration remains a significant challenge due to differences in observation perspectives, deficient data overlap, and often inhomogeneity in point distributions and densities. The challenge increases in complex forest environments due to the abundance of unstable features (e.g., leaves) and occlusions. Thus, the dynamic nature of forest scenes needs to be considered when applying registration methods on forest point clouds. In this paper, we overview the latest advancements in registering forest point clouds from multi-modal data acquisitions, aiming to discuss the strengths and weaknesses of the most used LiDAR registration methods for forest applications. To support our investigations, we benchmark two multi-modal registration methods especially designed for forest mapping against traditional global and feature-based approaches. Experiment assessments were conducted using two point clouds acquired from a permanent laser scanning and airborne laser scanning systems at a boreal forest plot.
Mapping urban green space of the 36 major cities in China using ZY-3 satellite images based on a feature robust to low-brightness pixels
Authors: Lu Chen , Du Xiaoyu , Wang Guanghui , Zhang Wei , Zheng Lijuan
Published: May 2024

Abstract. Urban green spaces (UGS), integral to modern urban life, play a pivotal role in shaping ecological, low-carbon, resilient, and livable cities. Understanding the microstructure and macro patterns of UGS is necessary but limited. In this context, we investigated the spatial patterns in the 36 major cities in China using Ziyuan-3 remote sensing images. We used a method for extracting UGS suitable for complex urban environments, and then selected 5 landscape pattern indices such as percent of landscape and patch density to analyse the spatial pattern of UGS in the 36 cities. The results show that: (1) The overall accuracy, recall, F1 value, and intersection over union of the extraction results of UGS were 94.62%, 94.11%, 94.36%, and 89.33% respectively; (2) Overall, the green space rate of the 36 cities exhibited a spatial distribution pattern of “high in the east and low in the west, low in the north and high in the south”; (3) Variations among cities were weak in terms of patch shape, but significant in patch density. The findings of this study cater to the escalating demands of urban planning and management.
Urban lakes change extraction using time series GaoFen-1 satellite imagery
Authors: Luo Gan , Jiang Wei , Liu Lingjia , Wen Qingke , Liu Shuo , Sun Weichao
Published: May 2024

Abstract. Urban lakes serve an indispensable role in maintaining the ecological balance of cities, ensuring flood safety, and providing recreational spaces for tourism. With the development of human activities and economic, the extent of urban lakes are inevitably influenced. Currently, the ability to detect detailed temporal changes in urban lake areas using high resolution data still has limitations. This study proposed a novel method by combining time series Gaofen-1 (GF-1) remote sensing data and random forest machine learning algorithm to explore the urban lakes change Zhushan Lake located in Wuhan. The research conducted the extraction of surface water for Zhushan Lake and its surrounding pit-ponds from 2013 to 2020. And then, a quantitative analysis of the characteristics and driving factors of lake changes is conducted. We find that (1) the accuracy of surface water extraction using the random forest classification method consistently exceeded 96%. The Kappa coefficient ranges from a minimum of 0.86 to a maximum of 0.99. (2) A noticeable decline was observed in the water areas of Zhushan Lake and its surrounding pit-ponds, predominantly along the northwestern shoreline and in the eastern pond regions. This decline is primarily attributed to pressures from building construction. The methodology proposed in this study is suitable for the area management of lakes in urban areas.
Long-term Satellite-derived Bathymetry of Arctic Supraglacial Lake from ICESat-2 and Sentinel-2
Authors: Lv Jinhao , Li Shaoyu , Wang Xiaoming , Qi Chao , Zhang Meng
Published: May 2024

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.
Leveraging Deep Learning for Automated Reconstruction of Indoor Unstructured Elements in Scan-to-BIM
Authors: Mahmoud Mostafa , Chen Wu , Yang Yang , Liu Tianxia , Li Yaxin
Published: May 2024

Abstract. Achieving automatic 3D reconstruction for indoor scenes is extremely useful in the field of scene understanding. Building information modeling (BIM) models are essential for lowering project costs, assisting in building planning and renovations, as well as improving building management efficiency. However, nearly all current available scan-to-BIM approaches employ manual or semi-automatic methods. These approaches concentrate solely on significant structured objects, neglecting other unstructured elements such as furniture. The limitation arises from challenges in modeling incomplete point clouds of obstructed objects and capturing indoor scene details. Therefore, this research introduces an innovative and effective reconstruction framework based on deep learning semantic segmentation and model-driven techniques to address these limitations. The proposed framework utilizes wall segment recognition, feature extraction, opening detection, and automatic modeling to reconstruct 3D structured models of point clouds with different room layouts in both Manhattan and non-Manhattan architectures. Moreover, it provides 3D BIM models of actual unstructured elements by detecting objects, completing point clouds, establishing bounding boxes, determining type and orientation, and automatically generating 3D BIM models with a parametric algorithm implemented into the Revit software. We evaluated this framework using publicly available and locally generated point cloud datasets with varying furniture combinations and layout complexity. The results demonstrate the proposed framework's efficiency in reconstructing structured indoor elements, exhibiting completeness and geometric accuracy, and achieving precision and recall values greater than 98%. Furthermore, the generated unstructured 3D BIM models keep essential real-scene characteristics such as geometry, spatial locations, numerical aspects, various shapes, and orientations compared to literature methods.
Fast and accurate Zero-defect manufacturing using collaborative real-time photogrammetry
Authors: Messina Francesco , Lingua Andrea Maria , Martino Alessio , Matrone Francesca , Maschio Paolo
Published: May 2024

Abstract. For the machining of industrial components, their accurate alignment on operative machines is a crucial step with significant impacts on the whole process, especially when dealing with large-scale elements. To achieve this alignment, a trained operator is needed along with expensive equipment, such as laser trackers, whose correct use requires the measurements of many significant points and is time-consuming. The lack of a precise alignment comes with possible incorrect parts machining, resulting in quality concerns and potentially expensive rework. To face this problem, collaborative real-time photogrammetry is an effective solution for guiding untrained users in correctly positioning various measured and codified components to generate the best solution for machining high-volume parts. The solution proposed in this contribution is part of the TACCO project, co-financed by EIT Manufacturing (co-funded by the EU). It takes advantage of the TACCO system, where auxiliary components (scale bars, dome-shaped components, coded targets and a cross-reference system) are placed on the object to be measured for their correct positioning in order to obtain an optimal photogrammetric reconstruction. This allows the reduction of the error ellipsoids on several uncoded targets, which are calculated through an iterative simulation carried out in a MATLAB environment. Unskilled operators are then guided through an Augmented Reality headset for their correct placement on the real-world object and for defining the positions and orientations from which to acquire images. This methodology will lead to an accurate measurement of the target points to be machined and, thus, a precise alignment process for the subsequent machining operations.
Utilizing InSAR for Surface Stability Monitoring in Mining Sites: A Case Study of Sukari Gold Mine in Egypt
Authors: Mohamadi Bahaa
Published: May 2024

Abstract. The monitoring of mining sites is crucial as a result of the safety and environmental issues linked to mining activities. Traditional surveying and monitoring tools exhibit limitations in terms of spatial coverage and are associated with various concerns regarding time and cost efficiency. Hence, the mining industry has shown increased interest in the time-series analysis of interferometric synthetic aperture radar (InSAR). This is primarily due to its precision and wide spatial coverage advantages. This study employed an InSAR technology, persistent scatterer interferometry (PSI), to identify potential geohazards within the Sukari gold mine (SGM), located in Egypt. A total of 23 images were collected from the ascending orbit of Sentinel-1 sensor between January 7, 2022, and November 3, 2022, for the SGM mining location. Results revealed relatively stable measurements in non-mining areas of the site, with a surface displacement within ±10mm per year in the line-of-sight (LOS) direction. However, several active mining operation areas showed significant surface motion. Various sections of the open pit of the SGM exhibited displacement, including the southwestern edge (-16.8mm) and the northeastern edge (35.7mm), and the highest velocity was detected at a section of the western edge, which demonstrated an annual displacement of -64mm in the LOS. Additionally, notable surface motion was observed in some parts of the two tailings storage facilities at the site. The results of this study illustrate the importance of InSAR techniques in mining site-wide coverage stability monitoring.
Triplet Epipolar Images for Spaceborne Three Linear Cameras
Authors: Pan Hongbo , Yang Shuang
Published: May 2024

Abstract. Epipolar images can reduce conjugate points searching from a two-dimensional to a one-dimensional space, which significantly improves the robustness and efficiency of dense matching. However, two images matched the lack of redundancy and suffered from occlusions. The third image could introduce two more stereos and improve the accuracy and completeness of the three-dimension reconstruction, which were adopted by spaceborne three linear cameras (TLCs), the ALOS PRSIM and ZY-3. In this study, we propose a new triplet epipolar images generation method for spaceborne TLCs. Triplet epipolar images were defined as any two of the three epipolar image pairs. The triplet epipolar geometry requires the imaging rays of any conjugate point to be coplanar. For high-resolution satellite images, parallel projection can be used to approximate the imaging geometry of satellite images. Therefore, a coplanar principal optical axis is a fundamental requirement for spaceborne TLCs. We propose a general workflow to generate triplet epipolar images (TEIs), which includes free-net bundle adjustment of TLCs, building a triplet epipolar geometry, correcting the y-parallaxes, and generating a rational function model (RFM) for TEIs. The ZY3-02 satellite images were used to validate the proposed method. The root means square error (RMSEs) of the free-net bundle adjustment in the image space was 0.185 pixels, which proved the fine intrinsic accuracy. After compensation, the RMSEs of the y parallaxes of the three epipolar image pairs were 0.295, 0.310, and 0.370 pixels. Owing to the simple geometry of the TEIs, the RMSEs of the RFM replacements were within 0.001 pixels.
Selected Driver Variables for the Simulation of Land-Use and Land-Cover Change for the Republic of Djibouti: A Study from Semi-Arid Region
Authors: Pandit Santa , Shimada Sawahiko , Dube Timothy
Published: May 2024

Abstract. This study aims to integrate driver variables with a land use change model (LCM) to explore their impact on the natural environment within the context of land-use changes in the Republic of Djibouti, considering possible Business-as-usual scenarios. Secondary data from 1990 and 2012 on land use land cover (LULC) were analyzed, with a 2022 map generated by adopting the same method of secondary data used (random forest classification in Google Earth Engine (GEE)) for validation. Eight key driver variables were utilized to model plausible future land cover (2035) for Djibouti. Statistical outputs and change maps from the LCM were compared to gauge historical change estimates and simulated scenarios. Analysis from 1990 to 2022 highlights significant land use and cover changes spurred by urbanization, environmental factors, and economic development. Barren land and bushland dominated, while built-up areas and water bodies expanded notably. Urbanization, agriculture, and climate change contributed to vegetation degradation, with declines in mangroves and increases in built-up areas. Water bodies also expanded during this period. Projections from the 2035 LULC map anticipate further urban expansion, underscoring the need for sustainable land management practices. In conclusion, comprehensive land-use planning, interdisciplinary approaches, and stakeholder engagement are deemed critical for addressing Djibouti's socio-economic and environmental challenges and steering towards a sustainable future. These simulated results offer valuable insights for regional governments to frame strategic policies and assess management actions for resource utilization amidst urbanization and population growth trends.
Robotics for Heritage Surveying: preliminary test on Leica BLK ARC & Spot® toward autonomous 3D mapping
Authors: Sammartano Giulia , Spreafico Alessandra , Tanduo Beatrice , Chiabrando Filiberto
Published: May 2024

Abstract. Today, robotics technologies are revolutionizing surveying and construction in AEC fields, enhancing precision, safety, and efficiency and also in the realm of Cultural Heritage knowledge and protection, the integration of cutting-edge technologies is reshaping consolidated surveying methods for documentation, especially in risk scenarios. One such innovation making waves is the use of mobile mapping systems, where automation and expediency are the determining factors in technological development, both in the direction of in indoor positioning (visual/LiDAR SLAM, UWB, etc.) and 3D mapping of known and unknown spaces. The BLK ARC by Leica, equipping the Agile Mobile Robot Spot® by Boston Dynamics, as a dynamic sensing platform, is here presented and discussed. BLK ARC is part of a diverse landscape of mobile mapping systems, each offering unique features and specifications tailored to different surveying needs, from handheld devices and wearable systems, to vehicle-mounted systems, the options vary in dimension, weight, price, and technical capabilities and the recent companies and research are largely focusing on them. A preliminary evaluation takes into consideration the dual dynamic-static 3D data-types, considering local and global accuracy of the 3D data delivered from the first experimental tests in a case study. Different metrics have been considered including acquisition time, precision, resolution, density, accuracy, roughness, and completeness of the acquired data.
Forest Plot Decay Level Classification from ALS-Derived L-moments
Authors: Sani-Mohammed Abubakar , Yao Wei , Fekry Reda , Wong Tsz-Chung , Heurich Marco
Published: May 2024

Abstract. Forests play key roles in climate regulation and essential environmental services for living organisms. This is why forests are the central focus of the United Nations (UN) Sustainable Development Goal (SDG 15). Thus, effective forest management is critical for forest sustainability and preservation. Remote sensing advancements have improved forest mensuration leveraging cost and time, contrary to the field surveying approach. Often, field data is required to validate remotely sensed results. However, circumstances in the forest may render field data collection impossible. This study applied LiDAR-derived L-moments to directly estimate and classify five forest plot decay levels, to understand forest growth dynamics in the absence of field data. Two L-moment-based rules were tested and evaluated for classifying the plot decay levels from ALS height returns. Our findings show that the first rule (Lcv = 0.5) classified decay Levels 1 and 2 at Lcv < 0.5 and Levels 3 to 5 at Lcv > 0.5, while the second rule (Lskew = 0) classified decay Level 1 at Lskew < 0, and Levels 2 to 5 at Lskew > 0. This indicates that, while discriminating plot decay levels, the L-moment-based rules can classify healthy forest areas and areas of deadwood of varying decay levels directly from ALS height returns. This can be convenient for forest managers to exploit for classifying plot decay levels and for mapping areas of large gaps for planning forest resources for effective forest management. Furthermore, the approach can equally be significant for assessing forest biomass, biodiversity, and carbon stock.
Research on Sugarcane Recognition based on Joint Spatial and Spectral Information from Satellite-Based Hyperspectral Imagery
Authors: Shi Yujun , Su Chengjie , Yue Tao , Jiang Lin , Fan Yajun , Rong Jing
Published: May 2024

Abstract. The total amount of sugarcane planting in China ranks third in the world, and Guangxi is the largest sugarcane planting base in China. In order to help the Guangxi government and industry better understand the annual planting distribution and scale of sugarcane in the whole region, it is convenient to optimize the planting structure and improve the efficiency of resource utilization, so as to ensure the stable development of Guangxi sugar industry. Based on the satellite-based hyperspectral image data of ' Zhuhai No.1 ', this paper carries out sugarcane recognition and area extraction, which fills the application gap of satellite-based hyperspectral image data in extracting sugarcane area in China. A globally optimized extended random walk model was used to identify and extract sugarcane by combining the spatial-spectral information of hyperspectral imagery. Firstly, the Initial probability estimation is performed using the LOR classifier, and then use the data after MNF dimensionality reduction to construct a weighted graph, and finally the probability optimization and recognition are carried out based on the extended random walk model. The results show that the spatial-spectral joint method based on extended random walk can effectively identify sugarcane, and the sugarcane recognition accuracy of 2021 year image can reach 91.4%, the misrecognition rate is 2.8%; the sugarcane recognition accuracy of 2020 year image can reach 90.5 %, the misrecognition rate is 3.1%.
Building footprint extraction from aerial images using an edge-aware YOLO-v8 network
Authors: Song Ziyu , Wang Weixi , Hao Zhenyu , Li Xiaoming , Tang Shengjun , Xie Linfu
Published: May 2024

Abstract. Building footprint extraction is a critical indicator for assessing urban infrastructure, and extracting building footprints from remote sensing imagery can have significant practical applications. However, achieving rapid and accurate extraction of building footprints remains highly challenging, especially in scenarios with complex scenes, dense building distributions, and small targets. The instance segmentation models of the YOLO series offer strong real-time performance, reducing considerable time and effort in practical applications. Therefore, we propose building footprint extraction based on an enhance YOLO-v8 network. This study focuses on three enhancements to the YOLO-v8 network to improve extraction accuracy. Building upon the YOLO-v8 framework, we have incorporated the Feature Pyramid Network (FPN) module into feature maps at all scales to efficiently propagate high-level semantic information. Additionally, we introduce the Triple Feature Encoder (TFE) module, which integrates spatial detail information from feature maps at three different scales to enhance the network's ability to extract multi-scale information. Finally, we explore the integration of the Prewitt model, a conventional edge detection operator, to assist in extracting edge features in target regions of feature maps. This integration aims to reduce the jagged edges frequently seen in the outcomes of the original YOLO-v8. Furthermore, the Prewitt operator's noise suppression capability helps mitigate the influence of non-target areas in the feature maps. The proposed framework achieves an instance segmentation accuracy of mAP50 is 84.6% and mAP50:95 is 51.4% on public datasets, outperforming the original YOLO-v8 network.