bluuebunny/crossref_metadata_2025 huggingface dataset:
Inversion Algorithm of Black Carbon Mixing State Based on Machine Learning
Authors: Tian Zeyuan , Wang Jiandong , Wang Jiaping , Liu Chao , Wang Jinbo , Zhang Zhouyang , Jin Yuzhi , Shen Sunan , Wang Bin , Nie Wei , Huang Xin , Ding Aijun
Published: October 2024

Abstract. Black carbon (BC) radiative impact is significantly influenced by its mixing state. Single-particle soot photometer (SP2) is a widely recognized instrument for quantifying BC mixing state. However, the derivation of BC mixing state from SP2 is quite challenging. Since the SP2 records individual particle signals, it requires complex data processing to convert raw signals into particle size and mixing states. Besides, the rapid accumulation of substantial data volumes impedes real-time analysis of BC mixing states. This study employs a light gradient boosting machine (LightGBM) to establish an inversion model which directly correlates SP2 signals with the mixing state of BC-containing particles. Our model achieves high accuracy for both particle size inversion and optical cross-section inversion of BC-containing particles, with R2 higher than 0.98. Further, we employed the Shapley Additive exPlanation (SHAP) method to analyze the importance of input features from SP2 signals in the inversion model of the entire particle diameter (Dp) and explored their underlying physical significance. Compared to the widely used Leading-Edge-Only (LEO) fitting method, the machine learning (ML) method utilizes a larger coverage of signals encompassing the peak of scattering signal rather than the leading-edge data. This allows for more accurate capture of the diverse characteristics of particles. Moreover, the ML method uses signals with a high signal-to-noise ratio, providing better noise resistance. Our model is capable of accurately and efficiently acquiring the single-particle information and statistical results of the BC mixing state, which provides essential data for BC aging mechanism investigation and further BC radiative effects assessment.
Dheed: an ERA5 based global database of dry and hot extreme events from 1950 to 2022
Authors: Weynants Mélanie , Ji Chaonan , Linscheid Nora , Weber Ulrich , Mahecha Miguel D. , Gans Fabian
Published: October 2024

Abstract. The intensification of climate extremes is one of the most immediate effects of global climate change. Heatwaves and droughts have uneven impacts on ecosystems that can be exacerbated in case of compound events. To comprehensively study these events, e.g. with local high-resolution remote sensing or in-situ data, a global catalogue of such events is essential. Here, we propose a workflow to build a database of large-scale dry and hot extreme events based on data from ERA5 reanalysis. Drought indicators are constructed based on evapotranspiration and precipitation data averaged over 30, 90 and 180 days. Extreme events are detected with the peak-over-threshold approach for the 1950–2022 period. Extremes and non-extremes are defined for daily maximum temperature at 2 m in combination with three drought indicators. In the last step, the spatiotemporal extent of the events is computed by a connected component analysis. The identified events are validated against extreme events reported in the literature.
Maritime Behaviour Anomaly Detection with Seasonal Context
Authors: Rybicki Travis , Masek Martin , Lam Chiou Peng
Published: October 2024

Abstract. Monitoring maritime traffic has become an important task for ensuring the safety of vessels, as well as the goods, and persons that they may be transporting. An active area of research is the modelling of expected normal vessel behaviour so as to detect subsequent anomalies in new data. Anomalies indicate that a vessel is not behaving in an expected manner and their detection can be flagged for further investigation to identify whether the vessel needs assistance or intervention. An important factor for some vessels in determining normal behaviour is seasonal context. However, current approaches typically do not incorporate seasonality into the model.In this paper, an approach is presented where seasonal context is incorporated into the behaviour model. Seasonal context is first incorporated into vessel trajectory data by encoding the month of year into a historic dataset. Following this, a model of normal behaviour is generated using a clustering approach, with DBSCAN used in this paper. Details of setting the DBSCAN parameters appropriately for vessel trajectory data are provided and four distance metrics explored. Resulting cluster models are evaluated in the context of using the model to classify previously unseen data as either fitting the model or constituting an anomaly. The experiments focus on using fishing vessels within two identified seasons to build the normal model, which is evaluated with a mixture of in season and out of season fishing and non-fishing vessel behaviour.
Poverty Mapping in India using Machine Learning and Deep Learning Techniques
Authors: Srishti Gulecha R. , Muthu Reshmi K. , Rishitha N. , Vani K.
Published: October 2024

Abstract. Poverty remains a persistent global challenge, that affects millions worldwide and hinders sustainable development goals. Poverty related data is traditionally collected by an on-the-ground household survey which is conducted once in a few years. Unfortunately, in India, the reach of this conventional data collection method has many limitations, and it is costly, time-consuming, and laborious. The study will assist in identifying areas of poverty and also the levels of poverty which will help policymakers in creating policies that will improve such areas. The research leverages a rich variety of data sources which includes satellite imagery, geospatial data, socio-economic surveys and Point of Interest (POI) data. To extract meaningful patterns and correlations within this diverse dataset, various machine learning and deep learning algorithms such as Decision Tree Regressor, Random Forest Regressor, Convolutional Neural Networks (CNNs) and Multi Layer Perceptron (MLP) are employed. With the help of Random Forest Regressor, the study was able to estimate the poverty at village/town level with a R2-score of 0.778.
Digital Twin-based Framework for Heat Stress Calculation
Authors: Cárdenas-León Iván , Morales-Ortega Rodrigoandrés , Koeva Mila , Atún Funda , Pfeffer Karin
Published: October 2024

Abstract. According to the KNMI Klimaatsignaal’21, the average surface temperature in The Netherlands has increased by 2.3°C between 1901 and 2020. Moreover, The Netherlands is also experiencing more frequent and intense heatwaves. Urban development significantly impacts the environmental conditions of a city, influencing thermal comfort and human well-being. To deal with these problems, municipalities across the country have been tasked to find ways to measure, understand, and find solutions to the increasing temperatures, specifically in urban areas. Because of this, several contrasting urban heat maps have been produced using different metrics and methods by different agencies. Koopman et al. presented a methodology for a standardized urban heat map at a 1-m spatial resolution to unify the stress tests by selecting the Physical Equivalent Temperature (PET) as a metric for heat stress. The PET is a key indicator in bio-meteorology, quantifying the combined effects of various environmental factors on human thermal perception. Despite its utility, widespread adoption of PET-based assessments by municipalities remains limited. To address this gap, this paper presents the development of a Digital Twin framework using PET analysis, enabling a collaborative, nondestructive, and cost-effective assessment of urban interventions’ impact on thermal conditions. Leveraging geoprocessing workflows and geospatial data, our framework allows for real-time PET calculations and scenario testing, facilitating informed decision-making by urban planners. The framework was tested and applied for Enschede, Netherlands, demonstrating its efficacy in visualizing current conditions, projecting future scenarios, and evaluating intervention strategies. Feedback from urban planners highlighted the tool’s usability and potential for enhancing community engagement in urban planning processes.
Research on the Method of Managing Personal Massive Photos Using Geodatabase
Authors: Cheng Tao , Xing Xuchao , Zheng Xinyan , Gao Yang
Published: October 2024

Abstract. In view of the limitations of the existing methods for personal massive photos management and browsing, a method of using geodatabase to manage and browse massive personal photos is proposed. Firstly, vector geodatabase is created and defined based on geographic information technology. Then, attribute information including the file name, photo date and time, photo point longitude, photo point latitude, location name, photo storage path and so on of each photo in the file system are extracted. Finally, based on the longitude and latitude of the photo point, the geospatial location data of each photo is generated, and written into the vector geodatabase together with the attribute information. Based on the storage path of photos in the file system, the links between the geospatial database and photos are established. Furtherly realize the attribute information and geographical space location management of massive photos, as well as the browsing and viewing of photos. Take personal photos as an example to introduce the data processing and verify the practicability of the method. The result shows that this method uses the idea of geodatabase to manage massive personal photos, which is conducive to reflect the geographical spatial distribution and density characteristics of the photos, and can use the time axis to connect the track of personal history, which is convenient for browsing and analyzing the experience.
Integration of Digital Repositories and Spatial Design within the Metaverse: the Evaluation of Features and Narratives to set Learning Environments on Cultural Heritage
Authors: De Marco Raffaella
Published: October 2024

Abstract. The Metaverse is rapidly advancing across cultural, professional, and service sectors, enhancing data compatibility and interaction. Its architectural design supports both visual interaction and information layers, characterising an ideal platform for the promotion of Digital Heritage. The digitisation of Cultural Heritage and the creation of Digital Repositories find complementarity in the Metaverse, which enables social interactions between Digital Heritage contents and virtual communities. This integration is pivotal for developing Learning Environments, where accessible virtual products can be combined with original digital storytelling and narratives.The paper presents methods and results from an Erasmus+ international school, which focused on using digital communication and 3D modelling tools to create immersive narratives on Mediterranean architectural modernity. Students from various Cultural Heritage disciplines designed virtual exhibitions within the Metaverse platform Spatial and open repository materials. The research concentrated on the application of Metaverse platforms for their spatial design and educational utility within the Heritage sector. The activities included developing digital narratives and interactive exhibitions enriched with multimedia data from digital repositories. The study discusses the limitations of customising visual materials, the importance of repository connections, user experience design, and the potential for future developments in Metaverse applications for Cultural Heritage learning environments and storytelling.
A unique Ruleset for Saltmarsh and Mangrove monitoring using Sentinel-2
Authors: Diti Israt J. , Rasel Sikdar M. M.
Published: October 2024

Abstract. Mangrove and saltmarsh vegetation are two important communities in the wetland ecosystem that require continuous monitoring considering their ecological threats and status. Remote Sensing observation is one of the tools to monitor this community. However algorithms are also important for the best sensor to map it accurately. Although there is some research about eCognition-based mapping, a unique rule set is important to monitor this community. Considering this research gap, a unique threshold-based ruleset has been generated in the Random Forest Model environment. A range of vegetation indices, individual bands of Sentinel 2 and Digital Elevation Model (DEM) data were tested in the feature selection method to find the best features for a Random Forest (RF) model. Top three variables were selected and those are (a) reNDVI_A, (b) VIRE_A, and (c) SWIR1 which gave 98.37% accuracy for the test data. A similar trend was found for the other three sites when they were compared with observed data. For site 1, Mangrove was 84.73%, Saltmarsh was 60.19%, and Mixed was 70.12% accurate. A similar trend was found for other three sites with overall accuracy 87.74 % for site-2, 66.23% for site 3, and 72.98% for site 4. A unique ruleset for wetland extent estimation will help to map a wide range of areas with a very minimum level of fieldwork. It will also reduce the cost of mapping done by visual observation, measurement and extensive fieldwork. The results of this work will provide the necessary insight and motivation for ecologists, and environmentalists.
sensorHUB – a Novel, Open-source Software Stack for Enhanced Accessibility and Secure Interoperability in IoT Project Management
Authors: Gackstetter David , Varoshi Parid , Asseng Senthold
Published: October 2024

Abstract. The Internet of Things (IoT) seeks to achieve seamless device connectivity and data sharing, thereby enhancing automation, efficiency, and insights in various fields of application. By delivering real-time data, IoT is essential for systems that aim to model and map the physical world to digital formats, such as digital twins or metaverses. In the recent years, there has been a surge in high-quality commercial IoT products available to various sectors. However, these solutions often come with high costs and proprietary interfaces, which hinder seamless interoperability. For users seeking cost-effective and flexible solutions, open-source software and international standards present viable alternatives. Nonetheless, despite technological progress, the adoption of open and standardized IoT software solutions is still predominantly seen among technology-affine groups or within research and development initiatives. We propose the sensorHUB, a novel IoT stack, which builds upon the Open Geospatial Consortium’s (OGC) data standard SensorThings API and enhances existing solutions towards more accessible, production-oriented solutions for diverse application domains and user groups. Key features include the provision of a new, state-of-the-art graphical user interface for managing data flows and the access to related software modules, and a secure and flexible user and access management.
Control Analysis of Building Under Construction from Indoor Mobile Mapping Systems
Authors: González-Cabaleiro Patricia , Túñez-Alcalde Rosa M. , Albadri Muataz S. A. , Fernández Antonio , Díaz-Vilariño Lucía
Published: October 2024

Abstract. During the execution phase of construction projects, quality defects arise due to various factors such as human intervention, technical aspects, logistical issues and environmental influences. Early detection of these defects is imperative to minimize their impact and prevent possible delays, increased costs and safety hazards. This paper explores the effectiveness of indoor mobile laser scanners for geometric quality control, adhering to established European technical norms and Spanish standards. Our developed approach presents a simple method for rapid assessment, capable of identifying vertical deviations in columns and walls, axis deviation in columns, flatness deviation in horizontal planes and level deviation using LIDAR data from Mobile Mapping Systems. This methodology has been validated through a real-world case study, demonstrating its practical applicability.
The Framework of GeoSOT-3D Grid Modeling for Spatial Artificial Intelligence
Authors: Han Bing , Qu Tengteng
Published: October 2024

Abstract. With the development of society, spatial artificial intelligence (spatial AI) research is gradually able to play a greater role. However, spatial AI has problems such as data alignment, poor interpretability, and cross domain learning. Therefore, this paper proposes an innovative GeoSOT-3D grid modeling framework for spatial AI research, which enhances the application capabilities of spatial AI. Grid modeling will be able to run through the upstream and downstream of spatial AI research, providing encoding calculations and spatial neighborhood embedding matrices for spatial data. This paper also uses task examples to demonstrate how to effectively organize and index spatial data using GeoSOT-3D grids and conduct spatial AI research. The use of GeoSOT-3D grids for spatial AI analysis has enormous potential and broad application prospects, which will help promote the further development and application of spatial AI.
A Data Model for Unrecognised Historical Neighbourhood (Case study: Telukbetung, Indonesia)
Authors: Ishar Shofia , Zlatanova Sisi , Yadav Yogender
Published: October 2024

Abstract. Unrecognised Historical places, which are locations with history but lacking official recognition, particularly prevalent in Southeast Asia, often face a dearth of accessible historical information. To tackle this challenge, there is an urgent need to commence the collection and documentation of historical data concerning Unrecognised Historical places. This initiative aims to prevent the loss of history, given that such sites are frequently subjected to rapid development and commercialization. To date, City Geography Markup Language (CityGML), an Open Geospatial Consortium (OGC) standard 3D data format, has been developed for presenting city models along with semantic data and is often used in the heritage domain for preservation. It aids in digital infrastructure development and the conservation of urban elements. Furthermore, it provides a mechanism to extend the standard data model to meet the requirements of various purposes. While several CityGML extensions tailored to the documentation of Cultural Heritage already exist, there is currently no extension specifically designed to address the requirements of Unrecognised Historical places. The aim of this paper is to identify a suitable approach for developing a new standardised data model that integrates geometric and semantic information, which is demonstrated for the modelling of Telukbetung, an old city in Bandar Lampung through time.
Accuracy Assessment of Land Use Land Cover Classification Using Machine Learning Classifiers in Google Earth Engine; A Case Study of Jammu District
Authors: Khan Sheeba , Bhardwaj Ashutosh , Sakthivel Mathivanan
Published: October 2024

Abstract. LULC (Land Use and Land Cover) involves classifying and describing different land types and their usage. Using satellite imagery for LULC mapping is increasing in remote sensing. This study focuses on Jammu district in India, situated between mountain ranges from north and south make it eco-sensitive zone. Expanding of human activity and loss of natural resources make it vulnerable if mismanaged. Study of LULC is important because of it and this study deals with efficiency and accuracy of various machine learning classifiers for LULC. This study uses machine learning classifiers - Random Forest (RF), Support Vector Machine (SVM), Gradient Boosted Trees (GTB), and Classification and Regression Trees (CART) - for the task, leveraging Google Earth Engine (GEE). Sentinel-2 satellite data from January 1 to March 31, 2023, with specific spectral bands (B4, B3, B2 & B8, B4, B3), were used for image preprocessing. Six classes: built-up, water, agricultural land, fallow land, forest, and barren land, each with 100 sample points, were used for classification. After training, the classifiers' accuracy was evaluated using Overall Accuracy and Kappa Coefficient. The results showed RF as the top performer with an overall accuracy of 99.36% and a Kappa coefficient of 99.11%, followed by SVM, GTB, and CART. This highlights the effectiveness of machine learning classifiers, especially RF and SVM, in accurately mapping LULC patterns in Jammu district, suggesting RF's potential as a reliable tool for remote sensing-based LULC mapping.
Efficient Rendering of Digital Twins Consisting of Both Static And Dynamic Data
Authors: Atanasov Aleksandar , Kottler Benedikt , Bulatov Dimitri
Published: October 2024

Abstract. The high number of vertices and faces present in a digital twin of a large scene poses a challenge when executing large-scale simulations that require the data to be rendered multiple times with slight changes such as the light conditions in a day-night cycle during a long period of time (weeks and even months). Adding urban morphology such as tree planting introduces a new layer of complexity due to the requirement for evaluating different tree configurations and their effect on the local environment in the context of urban heat islands. Using advanced rendering techniques such as splitting a single buffer into sub-regions with inplace updates as well as buffer orphaning multiple methodologies are evaluated.
Rapid Development of A Spatial Digital Twins Using Open-source Solutions
Authors: Lee Nicholas , Sun Chayn , Ho Serene , Wachowicz Monica , Acharya Debaditya
Published: October 2024

Abstract. City-model spatial Digital Twins are a three-dimensional virtual self-updating twin of a city which is updated by real-time data. This paper explores the effectiveness of open-source data and tools that can generate a Digital Twin to be hosted on a distributed system. A spatial Digital Twin was successfully generated from a city-model made from open-source spatial data with access to real-time data. The Digital Twin application was hosted on a three-tier systems architecture. The exception to the open-source data was a Digital Surface Model (DSM) obtained from a private source. However, the DSM was an optional component. The output only met the minimum requirements of a Digital Twin while the visualization was basic where the live data was limited to global weather data rather than city-specific data. The investigation indicated that even with little data, generating a 3D city scene with an automated live data flow meets the spatial Digital Twin’s minimum requirements according to Piroumian (2023). The main contribution of the study is highlighting methods for generating and hosting a spatial Digital Twin and its associated data onto the web through using a three-tier architecture to store, host and display the Twin onto the web. The areas identified for further research include investigating variations in the distributed systems for hosting and publishing a spatial Digital Twin onto the web. A secondary subject of investigation is exploring methodologies for improving the detail of the spatial Digital Twin’s data for areas that do not receive much live data.
Comparative Analysis of Water Body Extraction Accuracy Based on Thresholding Method
Authors: Lou Linjiang , Chen Chen , Li Minmin , Liu Kun
Published: October 2024

Abstract. With the continuous development of remote sensing technology, various methods for land water body extraction based on satellite remote sensing have emerged. The thresholding method, as a commonly used image segmentation technique, possesses advantages such as high efficiency and wide applicability, making it widely employed in water body extraction research. In this thesis, utilizing SPOT4 imagery, we conducted experimental comparisons of water body extraction using the Iteration thresholding algorithm, Kittler-Illingworth (KI) thresholding algorithm, and Otsu thresholding algorithm. The experimental results demonstrate that all three thresholding methods exhibit good performance in water body extraction, with the Kittler-Illingworth (KI) thresholding algorithm achieving the highest relative accuracy.
Surface Deformation Analysis by Fusion of Multi-temporal and Multi-source Data under Disaster Emergency Conditions
Authors: Shen Anpeng , Wei Shuangfeng , Wang Yuanyuan , Yang Zhaodong , Zhu Shouxin
Published: October 2024

Abstract. Rapid analysis of surface deformation is crucial for rescue operations following natural disasters. However, the lack of recent terrain data and the discrepancy between data types and field-collected data often hinder timely surface deformation analysis. To enhance data usability, this paper proposes an analytical method that integrates multiple sources of remote sensing data, including satellite data, oblique photography data, and LiDAR data. By merging oblique images with grayscale point clouds, true-color point clouds are generated. The method optimizes old data through threshold segmentation and median filtering, then converts and unifies the resolution of multi-source data via data interpolation. Elevation interpolation matrices are employed for overlay analysis, and a combination of bilateral filtering and threshold processing is used. This groundbreaking approach enables the completion of surface deformation analysis in emergency geospatial surveys and has been validated in various typical regions, demonstrating its application potential across different surface environments. Experimental results indicate that this method can quickly utilize multi-temporal and multi-source remote sensing data to effectively identify surface deformations following natural disasters.
Analysis of Behaviour Pattern of Bicycle-sharing Users Based on Complex Network: a Case Study of Beijing Downtown Area
Authors: Song Ruoyan , Jiang Jie , Guo Xian
Published: October 2024

Abstract. The introduction of the Bicycle-sharing System (BSS) has provided great convenience for residents for short travel in city. However, the parking lot planning and bicycle logistics distribution remain suboptimal, adversely affecting traffic order. Studying the spatial and temporal usage patterns can be helpful for better management of BSS. In this paper, we propose an analytical framework for detecting the bicycle-sharing pattern based on the community detection algorithms from complex networks to study the spatial-temporal patterns of usage.Firstly we identify the potential demand for bicycle among urban residents with a dataset from the BSS in Beijing downtown area. Then we construct a linkage network for modelling the BSS by applying community detection algorithms to identify regions of both high and low connectivity. Following this, based on the division of sub-areas, three indicators (graph density, cross-area travel demand, and confidence ellipses) are established for characterizing these sub-areas. Our findings include: (1) The usage pattern of the BSS within the downtown area consistently exhibits relatively stable clustering phenomena over time. (2) The usage pattern is related to the urban spatial structure, with significant differences between weekdays and weekends. (3) Residents tend to complete their cycling within the current sub-areas. (4) Generally, smaller sub-areas tend to have denser bicycle travel behaviour. These insights are vital for improving BSS parking lot planning and logistics distribution.
Operational Google-Earth-Engine Workflow to Monitor Irrigated Areas in a Semi-Arid Climate
Authors: Vos Kilian , Rasel Sikdar M. M. , Khan Azeem , Lu Yi , Shaikh Mustak , Gupta Vaibhav , Newstead Michelle
Published: October 2024

Abstract. Sustainable water resources management relies on advanced hydrological models calibrated from long-term satellite observations of water cycles. Monitoring water resources from Earth-observing satellites is enabled by cloud computing environments like Google Earth Engine (GEE) that support easy processing and analysis of archived data. While there are different satellite-based indicators of water resources availability, this paper focuses on mapping irrigated areas for each summer harvest season. The paper presents an automated workflow to extract yearly irrigated areas from imagery made public by Landsat and Sentinel-2. This method uses a normalised difference vegetation index (NDVI) to identify fields that exhibit higher vegetation greenness than their surroundings and are potentially being irrigated. As a first step, Google Earth Engine was used to create a seasonal maximum NDVI composite on which a grid-based thresholding algorithm was applied to identify irrigated areas in different parts of a catchment. The irrigated areas were then refined using morphological image processing and a quality-control step was conducted with ancillary datasets such as the most recent landuse classification, evapotranspiration data and crop phenology information. Finally, real-world use cases are presented to demonstrate the ability of satellite remote sensing to provide critical information on irrigation practices over the last 4 decades at the catchment scale.
Analysis of Influencing Factors of Green View Index Based on Street View Segmentation
Authors: Xu Xinyuan , Niu Lei
Published: October 2024

Abstract. The calculation of green view index in street view image has become an emerging choice for many researchers. This article proposes a PSAT-Net semantic segmentation model and specifically designs a pyramid pooling attention module. This module cleverly integrates channel and spatial information, implicitly implementing a three-dimensional attention mechanism, and significantly improving performance indicators. By performing detailed semantic segmentation on street view image, this article deeply analyzes the impact of different street view parameter configurations on green view index, and studies in detail how these parameters affect the visual perception of urban greening. The experiment found that: (1) The Green view index(GVI) of most sampling points decreases as the FOV increases. The increase in GVI is mainly affected by the crown width and pitch angle, and the high-value points of GVI are more easily affected. (2) Pitch angle usually causes a slight increase in GVI, which is mainly affected by the distribution of tall trees and low vegetation. Tall trees cause an increase in GVI, while low vegetation causes a decrease in GVI. Similarly, high value points of GVI are more likely to produce larger differences. (3) The Heading parameter has no obvious impact on the overall visual impact of GVI. The difference in independent sampling points comes from the angle between the lens acquisition direction and the road forward direction.These findings not only enrich the theoretical connotation of green view index research, but also provide valuable references for practical applications.
Assessing Photo-Voltaic Potential in Urban Environments: A Comparative Study between Aerial Photogrammetry and LiDAR Technologies
Authors: Yadav Yogender ,   Harshit, Kushwaha Sunni Kanta Prasad , Zlatanova Sisi , Boccardo Piero , Jain Kamal
Published: October 2024

Abstract. Urban development and population growth have significantly increased energy demands, predominantly met by non-renewable resources, negatively impacting nature and climate. Consequently, there has been a global shift towards renewable energy sources, with solar energy being widely adopted. Photovoltaic (PV) potential measures the usable electricity from solar energy using PV technology. With conventional methods, estimating PV potential involves converting 3D point cloud data to 2.5D elevation models, which can affect the accuracy of the estimation. A research gap exists in using adequate 3D point cloud datasets for PV potential estimation considering roof surface area and the surface normal vectors. This study compares aerial photogrammetry and aerial LiDAR point clouds for PV potential estimation against the DSM-based approach. Accurate PV potential estimation must consider solar incidence, roof area, azimuth, tilt angles, and PV efficiency. Traditional 2.5D methods often overlook crucial azimuth and tilt data, limiting the accuracy of the PV estimation. Converting 3D data to 2.5D may result in information loss, while 3D analyses offer higher accuracy. To investigate the mentioned gaps, this research aims to evaluate the capabilities of photogrammetry and LiDAR for urban PV potential estimation, highlighting their feasibility and accuracy over 2.5D methods.
Construction and Application of Place Name and Address Management System Based on Elasticsearch
Authors: Zhang Hongping , Huang Wei , Ding Lei , Tang Dejin , Wang Cong , Xu Yong , Wang Zhen
Published: October 2024

Abstract. Place names and addresses serve as essential references for locating information about people's living, production, and various activity sites. They are a crucial component of public geographic framework data and act as tools and bridges for social communication in daily production and life. This paper presents a comprehensive system for managing place names and addresses by integrating a multi-level address model, data synchronization between PostgreSQL and Elasticsearch, and advanced query capabilities to ensure efficient and accurate data management. The system implements full lifecycle management of place names and addresses, fully leveraging the advantages of relational databases and distributed search engines. It provides essential functions such as address standardization, real-time updates, maintenance management, conditional queries, and statistical analysis. It has been successfully implemented in the National platform for common geospatial information service, laying the foundation for the next steps in refining social governance based on place names and addresses, and further advancing the digital transformation of government operations.
A Lightweight Blind Obstacle Detection Network for Mobile Side
Authors: Zhao Jianghong , Liu Ziyu , Liu Ziling , Xu Ailin , Zhao Jifu
Published: October 2024

Abstract. China has the longest and widest distribution of blind corridors in the world, but in many cities they are virtually non-existent, with all kinds of obstacles affecting the movement of the blind. Thus, guaranteeing safe traveling for the blind has become an increasingly important topic. Some existing assistive traveling devices for the blind have problems such as poor portability and low-cost performance. With the rise of the mobility era and the rapid development of deep learning technology, the target detection of blind obstacles on cell phones has become feasible. In this paper, we take the target detection model YOLOv8n as the base network, redesign the neck of the network, use GS convolution as the basis, adopt one-time aggregation and cross-channel branching to build a lightweight module, and use the CARAFE operator as the up-sampling method, and stack the lightweight module with CARAFE operator to build a new feature fusion layer, forming a lightweight feature-aware enhanced target detection network. The results show that the accuracy still reaches 99.5% of the original network under the premise that the model size is reduced by 1.2%, and the improved network achieves a balance between accuracy and lightweight.
BG-LoA: A Benchmarking Framework for BIM/GIS Data Integration Based on Meta-Modelling Theory
Authors: Zhu Junxiang , Wei Ran , Yin Mengtian , Brilakis Ioannis
Published: October 2024

Abstract. There are various studies on the integration of building information modelling (BIM) and geographic information system (GIS) that convert BIM data into GIS data. These studies used different technologies, data standards, data models, and data formats such as RDF (Resource Description Framework), graph, CityGML, and Shapefile to address the data incompatibility problem between BIM and GIS. This makes it difficult to compare these studies for the purpose of assessing research innovation, and sometimes, improper comparisons were carried out between studies. To solve this problem, this study adopted the metamodeling theory used in model-driven software engineering (MDSE) and extended its 4-layer framework of level of abstraction. The new benchmarking framework, referred to as level of abstraction for BIM/GIS data integration (BG-LoA), has 5 levels, including meta metamodel (M3), metamodel (M2), model (M1), reality (data) (M0), and reality (real world) (M00). The common types of IFC (Industry Foundation Classes) conversion were analysed using the new framework, including IFC-to-CityGML, IFC-to-CityJSON, IFC-to-Shapefile, IFC-to-GML, IFC-to-3DTiles, IFC-to-RDF, IFC-to-IfcGraph, and IFC-to-UML. The result shows that the new framework can show the difference between those conversion types, which makes it easier for future studies to benchmark their work and assess level of contribution to the body of knowledge.
A global analysis of the fractal properties of clouds revealing anisotropy of turbulence across scales
Authors: Rees Karlie N. , Garrett Timothy J. , DeWitt Thomas D. , Bois Corey , Krueger Steven K. , Riedi Jérôme C.
Published: October 2024

Abstract. The deterministic motions of clouds and turbulence, despite their chaotic nature, have nonetheless been shown to follow simple statistical power-law scalings: a fractal dimension D relates individual cloud perimeters p to a measurement resolution, and turbulent fluctuations scale with the air parcel separation distance through the Hurst exponent, ℋ. However, it remains uncertain whether atmospheric turbulence is best characterized by a split isotropy that is three-dimensional (3D) with H=1/3 at small scales and two-dimensional (2D) with ℋ=1 at large scales or by a wide-range anisotropic scaling with an intermediate value of ℋ. Here, we introduce an “ensemble fractal dimension” De – analogous to D – that relates the total cloud perimeter per domain area 𝒫 as seen from space to the measurement resolution, and we show theoretically how turbulent dimensionality and cloud edge geometry can be linked through H=De-1. Observationally and numerically, we find the scaling De∼5/3 or H∼2/3, spanning 5 orders of magnitude of scale. Remarkably, the same scaling relationship links two “limiting case” estimates of 𝒫 evaluated at resolutions corresponding to the planetary scale and the Kolmogorov microscale, which span 10 orders of magnitude. Our results are nearly consistent with a previously proposed “23/9D” anisotropic turbulent scaling and suggest that the geometric characteristics of clouds and turbulence in the atmosphere can be easily tied to well-known planetary physical parameters.
Growing Old in the City: Challenges of Access to and Control of Urban Houses among Older Women in Low-income Suburbs of Bulawayo, Zimbabwe
Authors: Hungwe Chipo
Published: October 2024

This study addresses the struggles for housing among older women, including the meaning attached to ownership and control of urban houses among low-income households. It analyses the extent to which older women in low-income suburbs of Bulawayo, Zimbabwe, can age-in-place. I explain how the ideal of ageing in place for the nineteen women in this research (ages 60 – 90) is dependent on factors such as access to and control of one’s housing situation, which are impacted by whether or not they have good sibling and intergenerational family relationships. Family conflicts and unpleasant interactions with siblings, adult children, nephews, nieces, and grandchildren affect their chances of ageing well in their place of choice forcing some of them to begrudgingly retrace their steps back to the rural areas. These older women are poor and do not have reliable sources of income to look after themselves and the children under their care.
Design and Testing of a Web-Based Student Information Management System
Authors: Pasaribu Johni S , Argadikusuma Ilham S
Published: October 2024

Politeknik Piksi Ganesha is an institution of higher learning located in Bandung, Indonesia, specializing in engineering and technological curricula. The research's general objective is to describe the design and implementation of the web-based Student Information Management System (SIMS) to manage student data at Politeknik Piksi Ganesha Bandung. The functional and non-functional specifications are represented using Unified Modeling Language (UML) based on broad data collection through observation, interviews, and a literature review. It also entails black-box testing, which measures the system's performance against set criteria, focusing on the output without the details of its internal workings. The significant findings indicate that SIMS enhances highly administrative processes, accuracy in data, and good communication among stakeholders by applying clear UML diagrams of the system. This successful system development meets the institution's requirements and adds to the bar for other developments within the educational technology sector. The result has also emphasized the need for practical design methodologies and testing frameworks to develop reliable information systems to improve decision-making in academic institutions. The research has contributed significantly to the discipline because it has shown how different technological innovations can alleviate the problems surrounding managing student information at higher education levels.
Propuesta de un nuevo tipo de herramienta lexicográfica pedagógica para la producción en alemán y español como lenguas extranjeras.
Authors: Fernández Méndez Manuel , González Ribao Vanessa
Published: October 2024

En este artículo se toman como ejemplo algunos verbos españoles y alemanes del paradigma que lexicaliza el concepto de prestar (p.ej. alquilar, ausleihen) para explicar las bases y describir las principales características del recurso CombiDigiLex. Se trata de una herramienta lexicográfica digital multilingüe de lexemas verbales y deverbales del español, alemán y portugués, con una organización conceptual del léxico que permite acceder al mismo desde una perspectiva onomasiológica. Está basada en material empírico extraído de corpus lingüísticos y está especialmente destinada a cubrir las necesidades de las consultas que se efectúan con la finalidad de realizar una producción libre o traducción.
To Compare the Effectiveness of Vascular Exercises Versus Vascular Massage Therapy on Lower Extremity Function and Blood Circulation Among Female Subjects with Chronic Venous Insufficiency
Authors: Shenega K. , Koteesswari A. , Parthiban B.
Published: October 2024

OBJECTIVE: The aim of the study was to analyze the effectiveness of vascular exercises versus vascular massage therapy for the patient with the chronic venous insufficiency.
BACKGROUND OF STUDY: Chronic venous insufficiency is a common condition in the elder person, pregnancy women, obesity etc., The symptoms are pain, swelling, loss of ankle joint range of movement. Earlier studies have shown the benefits of exercise in the patients. The purpose of this study was to analyze the effectiveness of vascular exercises and vascular massage therapy among the chronic venous insufficiency patients.
METHODOLOGY: This is an experimental study of comparative type. This study was carried out in the faculty of physiotherapy department, Dr. M.G.R. educational and research institute after the approval from institution review board for 12 weeks. 20 subjects were included in the study based on the inclusion and exclusion criteria after getting their consent for participation. The subjects were divided into two groups by random sampling method. Ankle brachial pressure index [ABPI] and lower extremity functional scale [LEFS] is used for pre and post assessment. The subjects in group A performed vascular exercises for 30 mins/day for 3 days/week for 12 weeks. The subjects in group B performed vascular massage therapy in lower extremity for 30mins/day for 3day/week for 12 weeks
RESULT: On comparing pre-test and post-test within group A and group B on Ankle Brachial Pressure Index and lower extremity function scale score. Group A shows highly significant difference in mean values at P ≤ 0.001
CONCLUSION: The study revealed that vascular exercises was more effective in the treatment of chronic venous insufficiency compared to vascular massage therapy.

Key words: Chronic venous insufficiency, vascular massage therapy, vascular exercises.
Effect of Hand Exercise Programme on Pain and Grip Strength Amongst Post-Menopausal Women with Hand Osteoarthritis - A Randomized Clinical Trial
Authors: Kamble Prachi , Bhore Vrushali
Published: October 2024

Background: Hand osteoarthritis is heterogenous condition in which there is multiple joint involvement of the hands. Due to that functional ability of hand gets affected. In post-menopausal women, there are several complications occurs such as osteoarthritis, insomnia, cardio-vascular diseases, etc. The purpose of the study is to determine whether hand exercises can help postmenopausal patients with osteoarthritis in their hands. The main focus of the study was hand pain and grip strength which was directly affect the functionality of hand. The study was done for 2 months in which the exercises were divided in 3 stages on the basis of progression and weeks. The findings showed that after the treatment there is significant pain reduction and improvement in grip strength.
Aim: To study the effect of hand exercise programme on pain and grip strength amongst post-menopausal women in hand osteoarthritis.
Methodology: Total 50 Post-Menopausal female participants were selected from the community of Sangli-Miraj-Kupwad. Those participants were selected according to the inclusion criteria as Hand Pain, Morning Stiffness, weak grip.
Outcome Measures:
McGill’s Questionnaire
Hand Dynamometer
American/Canadian Osteoarthritic Hand Index
Result: The Result shows that in Hand Pain, Grip Strength and Hand Function there is a significant statistical reliable difference between the pre & post treatment values with p-value is less than the 5% significance level (i.e. 0.001 < 0.05).
Conclusion: The present study concluded that there is significant improvement in Grip strength and lowers the pain which simultaneously improved the patients Hand Function

Key words: Hand Osteoarthritis, Grip strength, Hand Dynamometer, Therapy putty
Evaluation of a Child-Friendly School Program in Developing Peace Education at KB Joglo Pati
Authors: . Salamah , Pranoto Yuli Kurniawati Sugiyo , Suminar Tri
Published: October 2024

The purpose of this study was to evaluate the child-friendly school program in developing peace education at KB Joglo Pati. The evaluation model used is CIPP (Context, Input, Process, Product). Qualitative methods were used in the evaluation research. Data were collected through interviews, observations and documentation at KB Joglo Pati. The research results obtained are: (1) The implementation of the child-friendly school program is carried out in accordance with the current needs, namely to prevent violence and discrimination against children at school and in the community. (2) The child-friendly school program in KB Joglo already has good facilities and infrastructure, human resources and financial resources to support the implementation of a child-friendly school program by developing peaceful education. (3) The learning process is interactive, inspiring, and fun, inviting students to actively participate, and learning is tailored to the interests, talents, and psychological development of students by using the regenerative peace and justice curriculum. (4) The results of the implementation of the program are quite good and have shown the principles of child-friendly schools with peace education through habituation of students' attitudes or behavior. The findings of this study are important for children to learn non-aggressive education towards problems as an alternative experience to fighting, and to develop peaceful education while creating a safe and peaceful learning environment so that children are happy and fulfill their potential.

Keywords: evaluation, CIPP, child-friendly school, Peace Education