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271	Topic D: Geospatial Data Integration	ABS-271
Improving Multi-Sensor Spectral Harmonization: Bandpass Adjustment for GRUS-1 and Sentinel-2 Muhammad Daniel Iman bin Hussain (a*), Masahiko Nagai (a) a) Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Japan *danieliman9897[at]gmail.com Abstract Sensor spectral response differences can introduce significant inconsistencies in reflectance values across satellite platforms, limiting the accuracy and interoperability of multi-source remote sensing analyses. This study presents the first reproducible framework for bandpass adjustment of the GRUS-1 satellite, aiming to harmonize its spectral reflectance with Sentinel-2 MSI, a widely used and radiometrically stable platform. Achieving this alignment enables more reliable downstream applications such as vegetation monitoring and change detection using GRUS imagery. To simulate spectral responses, we utilized three publicly available spectral libraries (USGS, ProSail, and Hyperion), which collectively represent diverse land cover types and reflectance conditions. These spectra were convolved with the relative spectral responses (RSRs) of GRUS-1 and Sentinel-2 to generate paired reflectance datasets. Initial analysis revealed a non-linear relationship between GRUS and Sentinel-2 reflectance, limiting the effectiveness of simple linear models. We therefore applied piecewise linear regression to adjust GRUS-1 reflectance values to match those of Sentinel-2. Model performance, evaluated using RMSE and R2, showed that piecewise regression substantially improved reflectance agreement across bands. To validate these findings, we used sampled reflectance data (approximately 1000 points) from GRUS and Sentinel-2 image pairs, ensuring seasonal diversity with at least one image per season. Evaluation on real satellite pairs confirmed the modeled adjustments, not only in terms of pixel-level RMSE and R2, but also through improved NDVI consistency between the two sensors. This work provides a practical spectral harmonization approach for GRUS-1, especially valuable in scenarios where spatial or temporal coverage is limited. Future work will explore advanced modeling techniques such as artificial neural networks to further improve spectral harmonization. Keywords: Bandpass adjustment, GRUS-1, microsatellite, hyperspectral, Sentinel-2 Share Link | Plain Format | Corresponding Author (MUHAMMAD DANIEL IMAN BIN HUSSAIN)

272	Topic D: Geospatial Data Integration	ABS-272
2D Coordinate Transformation Between Aerial and Satellite Images Using Helmert and Cubic Splines Methods Mehmet Arkali, Saziye Ozge Atik, Cengizhan Ipbuker Istanbul Technical University Abstract The integration of spatial datasets from various sources necessitates precise coordinate transformations to ensure geometric consistency and interoperability. This study presents a coordinate transformation conducted between a high-resolution Pleiades satellite image and an orthophoto of the Ayaza&#287-a Campus at Istanbul Technical University. A total of 43 conjugate control points were identified within both datasets to effectively model the spatial relationship between the two coordinate systems.Two distinct transformation methodologies were applied: the 2D Helmert similarity transformation and the cubic spline transformation. The Helmert model is a rigid-body transformation, characterized by four parameters: translations along the X and Y axes, a uniform scale factor, and a single rotation angle. Conversely, the cubic spline method utilizes higher-order polynomial functions to address local distortions in the coordinate relationships.The transformation parameters were estimated using least squares adjustment. The results from the Helmert transformation indicated root mean square error (RMSE) values of 1.506 meters in the X direction and 1.353 meters in the Y direction. In contrast, the cubic spline method demonstrated enhanced accuracy, yielding RMSE values of 1.458 meters in the X direction and 1.248 meters in the Y direction. These findings suggest that while the Helmert transformation is effective for global alignment, spline-based approaches are superior for capturing local spatial variations in coordinate systems. Keywords: Share Link | Plain Format | Corresponding Author (Saziye Ozge Atik)

273	Topic D: Geospatial Data Integration	ABS-275
Efficient Classification of Airborne LiDAR Point Clouds using PointNet++ with Scanning Line Segmentation Makoto Ishiwata(a*), Masafumi Nakagawa(a) a) Shibaura Institute of Technology, Japan *ah21040[at]shibaura-it.ac.jp Abstract Recently, various methods have been proposed for classifying point cloud features using machine learning. However, machine learning requires a high-performance computing environment. Representative examples include the outdoor segmentation datasets, such as SemanticKITTI and nuScenes. Processing both of these datasets requires multiple GPUs for processing due to their large size. This results in an extremely long learning time, making them impossible to handle them on commonly used computers. Therefore this study verifies the feasibility of classifying point cloud features using machine learning with a small dataset. We used an aerial LiDAR point clouds consisting of ordered points along scanning lines, that acquired in dense urban areas in Tokyo. We subjected approximately 2,000 points to point interpolation to align the point intervals. Then, we performed RANSAC plane estimation to correct distortion. Next, we performed DBSCAN and convex hull processing to label surfaces and prepare the dataset. We used a computer equipped with only a built-in GPU and PointNet++ for learning. Moreover, we evaluated the accuracy of the processing using point clouds from other areas. The overall accuracy rate was 0.63, and the accuracy rate was higher for the flat and consistent ground surfaces than for other surface features. This method offers a new approach to processing the classification features in airborne LiDAR point clouds by generating a 3D model from 2D cross-sections. Additionally, we demonstrated that high-precision maps could be more easily created from point clouds using machine learning, even without a high-performance processing environment. Keywords: 3D Mapping, LiDAR, point cloud classification, machine learning Share Link | Plain Format | Corresponding Author (Makoto Ishiwata)

274	Topic D: Geospatial Data Integration	ABS-20
Predicting Multi-Scenario Land Use Change and Soil Erosion in the Poyang Lake Eco-Economic Zone Based on Coupled PLUS-RUSLE Modeling Kaitao Liao (a,b*), Zelong Wu(a,c), Yuejun Song(a,d), Liang Hu(c), Hui Lin(b) a) jiangxi Academy of Water Science and Engineering, 1038 Beijing East Road, Nanchang 330029, China b) Key Laboratory of Poyang Lake Wetland and Watershed Research (Ministry of Education), School of Geography and Environment, Jiangxi Normal University, Nanchang 30022, China c) Jiangxi University of Water Resources and Electric Power, 289 Tianxiang Road, Nanchang 330099, China d) Jiangxi Key Laboratory of Watershed Soil and Water Conservation, 1038 Beijing East Road, Nanchang 330029, China Abstract This study focuses on the Poyang Lake Ecological Economic Zone in China, integrating multi-source remote sensing data and geographic information data. Utilizing the Revised Universal Soil Loss Equation (RUSLE) and the Patch-generating Land Use Simulation (PLUS) model, we systematically analyzed the spatiotemporal evolution patterns of soil erosion from 2000 to 2020 and simulated land use dynamics under different development scenarios for 2030-2035. Key findings include: (1) Land use changes from 2000-2020 were characterized by cropland reduction, forestland fluctuation, and built-up land expansion. Cropland changes were significantly constrained by topographic slope and population density, while forest dynamics correlated with precipitation and GDP distribution. (2) Soil erosion predominantly occurred at light to moderate levels, forming a ring-like distribution around Poyang Lake. The erosion intensity exhibited a fluctuating ^increase-decrease-increase^ pattern over two decades, with emerging risks of cross-intensity class transitions in later stages. (3) Projections to 2035 under three scenarios (ecological protection, cropland preservation, and natural development) all show significant reductions in total erosion area. The ecological protection scenario demonstrates superior mitigation of forestland erosion, while the cropland preservation scenario effectively controls moderate-to-severe cropland erosion. However, the natural development scenario still requires vigilance against localized erosion risks from built-up land expansion. These findings provide scientific bases for constructing regional ecological security barriers and formulating differentiated soil conservation policies, emphasizing the critical role of ecological protection in coordinating regional development. Keywords: Poyang Lake Ecological Economic Zone- Soil Erosion- RUSLE- Land use- PLUS- Share Link | Plain Format | Corresponding Author (kaitao Liao)

275	Topic D: Geospatial Data Integration	ABS-279
Hybrid Image Fusion of Multispectral and SAR Data Using Contourlet Transform and Attention-Based CNN for Cloud and Shadow Problems in Southern Coast of Peninsular Malaysia Syaifulnizam Abd Manaf, Norwati Mustapha, Nor Azura Husin, Raihani Mohamed, Siti Nur Aliaa Roslan Faculty of Computer Science and Information Technology Universiti Putra Malaysia 43400 UPM Serdang Selangor Malaysia Abstract Cloud and shadow contamination remain persistent challenges in optical remote sensing, severely limiting the usability of satellite imagery for time sensitive and large scale applications. This study proposes a hybrid deep learning based image fusion framework to enhance the quality of multispectral MS imagery affected by atmospheric disturbances by integrating complementary synthetic aperture radar SAR data. The fusion architecture leverages the multi resolution directional representation of the Contourlet Transform with the adaptive feature extraction capability of an attention based convolutional neural network CNN. This combination is designed to enhance spatial detail retention and spectral consistency, particularly under cloudy and low illumination conditions. Fusion quality is quantitatively assessed using eight widely adopted performance metrics: Correlation Coefficient CC, Universal Image Quality Index UIQI, Relative Bias Bias, Entropy ENT, Root Mean Square Error RMSE, Erreur Relative Globale Adimensionnelle de Synthese ERGAS, Structural Similarity Index SSIM, and Difference in Variance DIV. The proposed hybrid method is benchmarked against several traditional fusion techniques including Brovey Transform, Gram Schmidt Fusion, Intensity Hue Saturation IHS Transform, Principal Component Analysis PCA, Nearest Neighbor Diffusion NND, and Curvelet Transform Fusion. Experiments conducted on real satellite datasets focusing on the Pontian district along the southern coast of Peninsular Malaysia demonstrate that the proposed Contourlet plus Attention CNN model delivers superior performance in preserving spatial and spectral features while significantly reducing cloud and shadow effects. These findings underscore the potential of hybrid deep learning models for robust multi source image fusion in operational remote sensing, particularly in cloud prone coastal scenario. Keywords: Image Fusion, Synthetic Aperture Radar (SAR), Multispectral Imagery, Contourlet transform, Attention-based Convolutional Neural Network (CNN) Share Link | Plain Format | Corresponding Author (Syaifulnizam Abd Manaf)

276	Topic D: Geospatial Data Integration	ABS-34
An Adaptive and Modern Land Use / Land Cover Classification System for Indonesia Using Multi-Sensor Earth Observation Imagery and Data-Driven Techniques: Collecting Data and Training with Dashcam and Field Camera Hadi F., Wahyuddin Y., Sabri L.M. , Suprayogi A., and Ramdani F Universitas Diponegoro University of Tsukuba Abstract The limited availability of publicly accessible land use and land cover (LULC) training datasets in Indonesia presents significant challenges for verifying historical image classification results. Traditional validation methods often rely on costly and logistically demanding field surveys, which are frequently prohibitive in scope and time. To address this critical gap, this study introduces a low-cost, scalable approach for LULC training data collection using consumer-grade dashcams equipped with Global Positioning System (GPS) functionality. These devices capture georeferenced video data embedded with spatial and temporal metadata. A comprehensive Python-based application was developed-assisted by Claude.ai and deployed in the Google Colab environment-to automate the extraction and processing of dashcam video frames. The system performs key tasks such as converting video to images, applying optical character recognition (OCR) for text detection, storing metadata in a structured database, and enabling public deployment via pyngrok for collaborative access. The application features four core modules: (1) camera metadata correction, (2) land cover labelling, (3) spatial coordinate adjustment, and (4) export functionality in both CSV and GeoJSON formats. From a test dataset of 21,945 images, 6,796 (30.96%) required manual verification of camera-derived information, highlighting the importance of integrated quality control in automated workflows. The platform^s web interface enables multi-user collaboration, significantly accelerating data validation and labelling compared to conventional field-based methods. Additionally, an interactive dashboard offers spatial filtering and region-specific download options, enhancing accessibility and usability across a range of research domains. This prototype lays the groundwork for a robust, accessible, and cost-efficient LULC classification framework in Indonesia. Keywords: land cover classification, dashcam imagery, collaborative mapping, Indonesia datasets, automated processing Share Link | Plain Format | Corresponding Author (Fatwa Ramdani)

277	Topic D: Geospatial Data Integration	ABS-37
Fusing Sentinel-1 and Sentinel-2 Data in Google Earth Engine for Road Infrastructure Mapping in Data-Scarce and Conflict Environments Mali Shadrack Paul1, Mitsuharu Tokunaga2 1Department of Civil and Environmental Engineering, Graduate School of Engineering, Kanazawa Institute of Technology 2Department of Civil and Environmental Engineering, Graduate School of Engineering, Kanazawa Institute of Technology * mwashorg2020[at]gmail.com Abstract Accurate road infrastructure data is vital for planning, mobility analysis, and disaster response, yet in conflict-affected and data-scarce environments such as South Sudan, authoritative sources are scarce and often outdated. Traditional field surveys remain difficult due to insecurity and cost, while optical imagery is frequently obscured by persistent cloud cover. This study presents a cloud-resilient, low-cost workflow for road mapping in Juba County that fuses Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within Google Earth Engine (GEE). Imagery from December 2024 to March 2025 (Sentinel-2) and the full 2024 year (Sentinel-1) was composited using pixel-wise median stacking with selected bands (B2, B3, B4, B8, B11, B12). Four fused tiles were exported to QGIS for marging, visualization, digitization, and comparison with OpenStreetMap (OSM), Geofabrik, and Google Satellite data. A Select-Zoomed-In road network visibility analysis (RNVA) demonstrated enhanced detection of paved and unpaved roads compared to single-source data. Results revealed numerous unmapped segments and outdated classifications in existing datasets. The integration of Informed Volunteered Geographic Information (IVGI), derived from the researcher^s local knowledge of Juba roads, further improved classification accuracy. The outputs provide a GeoAI-ready dataset for future automated road surface detection, contributing to closing data gaps in fragile regions. Keywords: Data-scarce environments, GeoAI, Informed Volunteered Geographic Information, Road mapping, Sentinel fusion Share Link | Plain Format | Corresponding Author (Mali Shadrack Paul Aburi)

278	Topic D: Geospatial Data Integration	ABS-294
Integrating Multi Sensor Satellite Data for Urban Heat Island and Vegetation Dynamics Analysis in Bali Putu Abel Nugraha Ardyan*, A. Muh. Tegar Juliarga Amrul, and Ilham Alimuddin a & b) Geological Engineering Departmen, Faculty of Engineering, Hasanuddin University, Gowa, Indonesia Abstract Urban heat island (UHI) dynamics in tropical, monsoon-influenced environments require integrated, multi-source observations to capture complex diurnal and seasonal variability. This study integrates MODIS daytime and nighttime land surface temperature (LST), Landsat 8 thermal observations, and Sentinel-2 normalized difference vegetation index (NDVI) to evaluate spatio temporal patterns of UHI intensity and vegetation dynamics across Bali Province, Indonesia. Two seasonal windows were examined, May to July 2024 (dry season) and December 2024 to February 2025 (wet season) to characterize monsoon-driven contrasts. Satellite-derived thermal and vegetation metrics were processed, analyzed, and mapped across the study area, with key thematic outputs presented as LST and SUHI maps, NDVI distributions, and summary figures. Results show pronounced seasonal contrasts: SUHI signals are weak or slightly negative during the dry season but become strongly positive in the wet season, with localized SUHI values reaching about 9 degrees Celsius in highly urbanized areas. Daytime and nighttime phases exhibit differing responses, underscoring the need to consider both diurnal components. Vegetation consistently moderates surface temperatures, with higher NDVI associated with lower LST, while densely built and coastal zones correspond to higher temperatures, reflecting local controls such as urban morphology, street-canyon effects, and coastal proximity. The multisensor integration reconciles differences in spatial resolution and temporal sampling across platforms, improving spatial coherence of thermal patterns and enabling more reliable detection of spatial hotspots and seasonal shifts that single-sensor analyses may underrepresent. Analytical outputs include composite LST maps, SUHI metrics, NDVI distributions, and comparative summaries that together characterize spatial heterogeneity and temporal dynamics. These consolidated outputs offer practical, data-driven i Keywords: Geospatial Data Integration, Multisensor Analysis, Remote Sensing, Urban Heat Island, Vegetation Dynamics Share Link | Plain Format | Corresponding Author (Putu Abel Nugraha Ardyan)

279	Topic D: Geospatial Data Integration	ABS-305
Evaluating Urban Accessibility within the 20-Minute City Framework Using GIS: The Case of Ulaanbaatar, Mongolia Azzaya Byambajav, Altankhishig School of Natural Sciences, School of Sciences, National University of Mongolia, Ulaanbaatar, Mongolia Abstract In the modern age, urban planning is increasingly required to address a range of complex issues, such as improving residents, quality of life, enhancing accessibility, promoting environmental sustainability, and reducing dependence on private vehicles. This research evaluates the walkability and accessibility within a 20-minute walking radius in selected areas of Ulaanbaatar, focusing on how easily residents can reach essential daily services such as healthcare, educational institutions, retail outlets, and green spaces. To assess service accessibility, density, and distribution, a heatmap was generated using QGIS, based on the geolocations of service units and institutional facilities across Ulaanbaatar is residential zones. This spatial visualization offers a more accurate representation of how services are distributed throughout the city. Furthermore, time-based catchment areas were analyzed using the Service Area Analysis tool in ArcGIS Pro to evaluate service accessibility within the 20-minute walking threshold. The results indicate a highly centralized concentration of services in the city center, whereas peripheral districts demonstrate significantly limited access to essential services within walking distance. This study enables the geographic identification of both well-served and underserved areas in Ulaanbaatar in terms of walkable access to key services, providing valuable insights for promoting more equitable and sustainable urban development. Keywords: GIS, Urban Accessibility, 20-Minute City, Service Area Analysis, Ulaanbaatar, Sustainable Urban Planning Share Link | Plain Format | Corresponding Author (Azzaya Byambajav)

280	Topic D: Geospatial Data Integration	ABS-309
A Landslide Predicting System Integrating Transient Hydrologic-stability Model And Precipitation Forecasts Hui Chen(a*), Chih-Yuan Huang(b), Shou-Hao Chiang(b) a)Master of Science Student in Department of Civil Engineering, National Central University b)Associate Professor in Center for Space and Remote Sensing Research, National Central University Abstract Landslides pose a significant hazard in Taiwan, primarily driven by geological conditions, rainfall patterns, seismic activity, and human development. Among these factors, rainfall is one of the primary external triggers, as it directly alters slope hydrology. This study aims to develop a landslide forecasting system that integrates a transient hydrologic-slope stability model with rainfall forecasts to simulate slope responses under predicted rainfall scenarios. The stability model is based on SHALSTAB (Shallow Landslide Stability Model) and has been modified to incorporate a dual-layer soil structure. A fracture layer between the soil and bedrock is introduced to better represent subsurface hydrological processes. By coupling this framework with a time-series soil moisture simulation, a transient hydrologic-slope stability model is constructed to dynamically link hydrological changes with slope stability. The Yusuei Watershed in Kaohsiung City was selected as the study area, with the model evaluated using Typhoon Lupit (August 2021). Calibration was performed based on observed hourly rainfall, followed by validation with historical forecasts. Predicted landslide timing and locations were compared to inventory data for performance assessment. Future work will extend the model to varied geological settings and integrate it into a real-time system using rainfall forecasts and trigger mechanisms to enable timely simulation and risk assessment, supporting landslide forecasting and emergency response. Keywords: Landslide- Fracture layer- Transient hydrologic-stability model- Landslide forecasting system- Yusuei watershed Share Link | Plain Format | Corresponding Author (HUI CHEN)

281	Topic D: Geospatial Data Integration	ABS-56
A GIS-based spatial data structure for integrating multi-resolution terrain data from lunar orbiter and rover: Application to Lunar Rover Path Planning and Operation Soomin Kim(a), Jaeyoung Lee(a), Junho Gong(b), Taehoon Kim(b), Hyusung Shin(b), Sungchul Hong(a*) a) Program in Smart City Engineering, Inha University, Incheon 22212, Republic of Korea * schong[at]inha.ac.kr b) Department of Future & Smart Construction Research, Korea Institute of Civil engineering and Building Technology, Goyang 10223, Republic of Korea Abstract The discovery of water-ice and rare resources on the lunar surface, particularly within the permanent shadowed regions, has increased international interest in robotic exploration, ultimately aiming at constructing the lunar base. Lunar exploration primarily relies on observation datasets collected by lunar orbiters, of which terrain data is used to support landing site selection and global path planning. However, the inherently insufficient resolution of orbital terrain data restricts their applicability for rover operations and infrastructure development. Therefore, to facilitate high-precision terrain analysis and to ensure the safe and efficient operation of rovers, it is essential to incorporate high-resolution terrain data acquired during rover traverses. This study proposes a GIS-based spatial data structure that integrates both low- and high- resolution terrain data from lunar orbiters and rovers. The proposed structure is a quadtree-based 8-directional network that allows hierarchical integration of terrain data at different resolutions and provides a topological basis for fine-grained path planning. Each node stores elevation, and each link encodes distance and slope. Path planning is performed using the A* algorithm, and an initial route is generated from orbital data. As the rover progresses, high-resolution terrain data updates the terrain and path. Moreover, the network can be converted into a mesh, which supports 3D visualization of terrain and traversal paths and enhances interpretability. The proposed structure was validated on a simulated lunar terrain. Using high-resolution terrain data acquired along the rover^s path, obstacles undetected in the low-resolution orbital terrain data were successfully identified, and the planned path was updated. Furthermore, the rover-based terrain data was confirmed to be integrated with the orbit-based terrain data. The proposed structure can incorporate additional observational datasets such as temperature and resource distribution. This capability is expected to support decision-making in unmanned rover navigation, lunar terrain modeling, and infrastructure development. Keywords: spatial data structure, GIS, lunar exploration Share Link | Plain Format | Corresponding Author (Soomin Kim)

282	Topic D: Geospatial Data Integration	ABS-57
A SEMI-AUTOMATIC METHOD FOR DETECTING BURNED AREAS AND DATING HISTORICAL FIRES USING LANDSAT DATA Ana Carvalho Edith Cowan University Abstract Accurate historical fire records are crucial for understanding fire impacts and guiding forest management However many fire history databases suffer from incomplete or inaccurate fire boundaries and dates limiting their usefulness Landsat with its 30 meter spatial resolution can detect fires as small as 009 hectares and has the potential to fill data gaps and improve historical fire records However its use has been limited by labor intensive processing especially when fire dates are unknown requiring advanced thresholding and machine learning classification methods. This study presents a methodology using Landsat imagery to detect burned and unburned areas within registered fire perimeters and estimate accurate fire start and end dates from 1990 to 2021 The fire history database FHD shows signs of stabilization in the number and size of polygons and satellite derived burned map areas However data agreement between the FHD and LST mapped burned areas decreases with polygon size. The LST method identified dates for 308 of 340 fires increasing recorded dates by 44 percent MEF values for start and end dates were 098 of agreement with the FHD with a statistically significant post 2000 improvement The MDS method identified dates for 68 percent of 253 fires while LST identified 87 percent Post 2000 both methods perfectly aligned with FHD records MDS struggled to detect fires smaller than 150 hectares whereas LST successfully captured both small and large fires. LST showed superior performance in enhancing fire records especially for detecting small fires and multiple date ranges Automating burned area detection and fire date estimation using Landsat and API integration significantly improves analysis efficiency and accuracy Keywords: Historical fire records, Landsat imagery, Burned area mapping, Fire date estimation, Machine learning classification Share Link | Plain Format | Corresponding Author (Ana Carvalho)

283	Topic D: Geospatial Data Integration	ABS-313
Integrating Geospatial Data for Local Economic Planning: A Case Study of Culinary Businesses in the Coastal Area of Parepare City, Indonesia Mulyawan M.R.1*, Yanaru.1, Jouhari M.I.1, SN S.A.1, Rahmat M.A.Z.1, Wibowo A.K.1, Nurdin N. 2,3, Aushaf S.T. 3 1Department of Marine Science, Hasanuddin University, Makassar, 90245. Indonesia 2Department of Remote Sensing and Geographic Information System, Vocational Faculty, Hasanuddin University, Makassar 90245. Indonesia 3Research and Development Center for Marine, Coast and Small Islands, Hasanuddin University, Makassar 90245. Indonesia *raidoidho[at]gmail.com Abstract Urban coastal areas in Indonesia are dynamic regions with diverse formal and informal economic activities that contribute significantly to the livelihoods of local communities. However, the lack of structured and comprehensive spatial data poses a challenge to inclusive and evidence-based regional planning in this region. One rapidly growing sector in coastal areas is the culinary business, although its geospatial characteristics have not been documented systematically. This study aims to identify the spatial distribution and attributes of culinary businesses in the coastal area of Parepare City, South Sulawesi, through a mobile GIS-based survey. Data from 289 business locations across 11 coastal sub-districts were collected, documenting attributes such as business type, physical facilities, signature dishes, and halal certification status. The data were analyzed using various spatial visualization methods, including choropleth maps to show the number of businesses per sub-district, 100- to 500-meter buffer analysis to evaluate spatial distribution relative to the surrounding environment, and heatmaps to map the concentration of culinary businesses in specific areas. The results show that traditional food stalls are the most dominant business type, although the majority still operate with limited facilities and are not officially halal-certified. Meanwhile, businesses such as cafes and container shops have more organized facilities but still face challenges in terms of legality and competition. The highest business concentration was found in sub-districts with a significant number of business locations, indicating a pattern of economic agglomeration in the coastal areas. This study emphasizes the importance of integrating geospatial data into local economic planning and development, particularly in coastal areas, to support equitable service distribution, improve business quality, and adapt policies to the socio-economic dynamics of the community. Keywords: Mobile GIS, culinary businesses, spatial analysis, coastal areas, Parepare City. Share Link | Plain Format | Corresponding Author (Moh. Ridho Mulyawan)

284	Topic D: Geospatial Data Integration	ABS-67
Bridging the Gap: Visual Evaluation of AI-Based Land Use Maps in Support of Local Development Planning Nurul Hidayah Y., Nurhaziyatul Adawiyah Y. and Norzailawati M.N. Kulliyyah of Architecture and Environmental Design, International Islamic University of Malaysia (IIUM), Malaysia Abstract Existing land use maps are one of the most crucial elements in preparing development plans, such as the Local Plan. These maps are typically used in conjunction with proposed land use zoning maps, which illustrate the intended future land uses based on the area^s planning policy, to assist urban planners in making informed decisions that shape the direction of urban development. Therefore, having an accurate existing land use maps is very important. Currently existing land use maps in local plans are derived from various sources, such as field surveys, satellite imagery, and existing databases from the planning department. However, these methods rely heavily on human intervention, is time-consuming, labour-intensive, and often not updated regularly. With the growing accessibility of readily available AI-based products that require no pre-processing, such as the Dynamic World dataset (developed by Google and the World Resources Institute) and the Global Forest Change dataset, this paper explores the potential of these off-the-shelf AI-based products in supporting local planning practices in Malaysia. This study uses Kuala Lumpur as its case study. These AI-based land cover datasets were accessed and analysed using Google Earth Engine, along with Kuala Lumpur zoning map, which served as the official reference. A visual assessment was conducted to identify the spatial mismatches between the AI-based land cover datasets and the Kuala Lumpur zoning map. Preliminary findings show that while both the Dynamic World dataset and the Global Forest Change dataset are able to perform reasonably well in detecting the zoning categories, their fixed classification categories, limited thematic detail, and the medium-resolution satellite imagery present significant limitations to be integrated into the urban planning practice. This study contributes to a deeper understanding of the potentials and limitations of current AI-based products and the requirements needed for them to support the preparation of development plans. Keywords: AI-based, Land Cover Classification, Pre-trained Land Cover Maps, Urban Planning, Zoning Map Share Link | Plain Format | Corresponding Author (Nurul Hidayah Yahya)

285	Topic D: Geospatial Data Integration	ABS-70
TerraLynx 3D: A WebGIS-Integrated Terrain and Sediment Modeling Framework for Dormant Hazard Monitoring in Cameron Highlands Mohamad Faisal Mohamed Yusof (a*), Syahidah Fariza Moh Esa (a), Siti Muazah Md Zin (b), Nurul Nadia Abd Malek (c) and Hasni Halim (d) (a) Research Officer, ICT Development and Geoinformatics Division, Malaysian Space Agency, Malaysia *faisal[at]mysa.gov.my (b) Research Officer, Strategic Application Division , Malaysian Space Agency, Malaysia (c) Research Officer, Strategic Planning and Communication Division , Malaysian Space Agency, Malaysia (d) Director, ICT Development and Geoinformatics Division, Malaysian Space Agency, Malaysia Abstract Since 1961, a total of 689 landslide incidents have been recorded in the Cameron Highlands, highlighting the serious and persistent threat of slope failures in this environmentally sensitive highland region. While some landslides remain active, many are dormant for years and may be suddenly reactivated by heavy rainfall or changes in land use. Despite the high risk, there is currently a lack of a dedicated 3D web-based platform for monitoring and visualising these dormant landslide-prone areas in the Cameron Highlands. This study addresses that gap by introducing TerraLynx 3D, an interactive 3D WebGIS system specifically designed to support near real-time monitoring. The system integrates multiple spatial parameters, including soil type, land cover, slope angle, rainfall distribution, and known critical slope locations. Sediment yield modelling is conducted using the Universal Soil Loss Equation (USLE) to identify zones with high erosion potential that may signal landslide reactivation. These outputs are processed and published via a 3D web environment. The system architecture features a backend GIS model, a Web-GIS environment, and is developed using multiple programming languages, including PHP, JavaScript, HTML, and CSS. Users can interact with the platform to explore terrain conditions, switch data layers, analyse slope profiles, and retrieve attribute data via pop-up windows. This tool empowers local authorities, planners, and researchers with an accessible and immersive way to monitor landslide risk areas in near real-time. By bridging GIS-based sediment analysis with 3D WebGIS technologies, the system enhances situational awareness and supports more effective land management and disaster preparedness strategies for the Cameron Highlands. Keywords: Dormant Landslide, 3D WebGIS, USLE (Universal Soil Loss Equation), Soil Erosion Modelling, Slope Stability Monitoring Share Link | Plain Format | Corresponding Author (MOHAMAD FAISAL BIN MOHAMED YUSOF)

286	Topic D: Geospatial Data Integration	ABS-331
A Study on the Platform of Monitoring and Early Warning of Poyang Lake Wetland Ecosystem Hui Lin and Chaoyang Fang Jiangxi Normal University: Professor, School of Geography and Environment, Jiangxi Normal University, China Abstract To implement scientific governance and protection of the Yangtze River and establish a National Ecological Civilization Demonstration Zone in Jiangxi, it is imperative to achieve coordinated development of human-water interactions in the Poyang Lake Basin. It is urgently needed to establish a real-time monitoring system for the ecosystem change of Poyang Lake wetland and the ecological safety of the basin. Objectives of this study are rapid, easy and cyclical acquisition of wetland area retention and monitoring of wetland breaching using remote sensing data, real-time monitoring of rare migratory birds^ habits and automatic analysis and recording of their habitats using surveillance, and the integration of IoT data and survey data for occasional assessment of wetland ecosystem health in terms of functional, biological and national standards. An integrated ^Space-Sky-Ground^ sensor network has been developed for Poyang Lake as the key result of this study. Keywords: Poyang Lake, Ecological System, Remote Sensing, Space-Sky-Ground sensor network. Share Link | Plain Format | Corresponding Author (Hui Lin)

287	Topic D: Geospatial Data Integration	ABS-336
Integrating PPP-AR into UAV-based Photogrammetry: Constellation and Accuracy Evaluation Mehmet Arkali, Muhammed Enes Atik ISTANBUL TECHNICAL UNIVERSITY Abstract The integration of Global Navigation Satellite System (GNSS) technology into unmanned aerial vehicles (UAVs) has significantly improved the positional accuracy of photogrammetric products. The availability of multiple GNSS constellations has greatly improved positioning capabilities. At the same time, advances in space geodesy and satellite technology have contributed to the development of new methodologies aimed at improving the accuracy of GNSS. Previous studies using GNSS-integrated UAVs have primarily focused on relative positioning techniques. However, there has recently been a shift towards the use of precise point positioning (PPP), a special form of absolute positioning. The PPP method enables the accurate estimation of 3D coordinates using GNSS data obtained from a single receiver operating at single or multiple frequencies. This study aims to investigate the performance of various GNSS constellation combinations in UAV-based photogrammetry using the PPP-AR (PPP-Uncertainty Resolution) technique. Thus, the visibility and number of satellites in each constellation, as well as their effect on positioning, have been examined. The PPP technique utilizes code and phase ambiguities, as well as precise satellite orbit and clock products, and can be applied in real-time or through post-processing. The ability of a single GNSS receiver to determine global positioning makes the PPP technique particularly noteworthy. With advancements in multi-frequency and multi-GNSS capabilities, positioning accuracy has improved due to the increase in the number of observable satellites and frequencies. The PPP technique has been utilized for many years to meet basic positioning needs, as well as for geodetic purposes and various scientific applications. In UAV-based photogrammetry studies, the PPP method is accepted as an alternative to RTK and PPK techniques. Keywords: UAV, GNSS, PPP, Photogrammetry Share Link | Plain Format | Corresponding Author (MEHMET ARKALI)

288	Topic D: Geospatial Data Integration	ABS-91
The Potential Sink City on Coastal Cities Using Recurrent Neural Network (RNN) in Malaysia Nur Hasnieda Zawawi, Norzailawati Mohd Noor Department of Urban and Regional Planning, Kulliyah of Architecture and Environmental Design, International Islamic University Malaysia Abstract Global warming is a major issue often linked to its effects on the ocean, such as rising sea levels. Malaysia is one of the countries highly exposed to the risk of climate change, particularly the significant rise in sea level which contributes to the phenomenon of sinking cities along its coastal urban areas. In Malaysia, Kelantan is one of the most exposed states because it faces the South China Sea and experiences annual monsoon phenomena with heavy rain, strong winds, and high waves. These weather conditions, combined with rising sea levels, increase the risk of flooding, land subsidence, and loss of coastal land in its low-lying areas. This study aims to determine the areas that are potentially impacted by sea level rise, which may result in the appearance of sinking cities in Kelantan. To assess sea level rise along the Kelantan coastline, the research implemented the Recurrent Neural Network (RNN) method through MATLAB applications to forecast future sea level changes. The results showed that by the year 2050, the sea level is projected to rise at a rate of 6.4 mm/year, resulting in a total increase of approximately 0.32 m over the 50-year period from 2000 to 2050. The rise in sea level is caused by global warming and climate change, with high temperatures melting ice and expanding seawater. Coastal zone management plays a crucial role in reducing infrastructure damage, land loss, and flooding from sea level rise through strategic land use planning to protect coastal areas sustainably. Keywords: sinking city, coastal area, climate change, sea level rise, Recurrent Neural Network (RNN) Share Link | Plain Format | Corresponding Author (Nur Hasnieda Zawawi)

289	Topic D: Geospatial Data Integration	ABS-347
Airshed-Based Source Apportionment of Delhi Air Pollution Using WRF-Chem and Back Trajectory Analysis Prakriti (1*), Asfa Siddiqui (1), Hareef Baba Shaeb Kannemadugu (2), Gokul R Kamath (1), Raghavendra Pratap Singh (1), Prakash Chauhan (2) 1) Indian Institute of Remote Sensing, ISRO, Dehradun, Uttarakhand, India 2) National Remote Sensing Centre, Hyderabad, Telangana, India *prakriti3896[at]gmail.com Abstract The intensification of human activities has significantly deteriorated air quality in densely populated urban centers such as Delhi, leading to severe health and urban climate challenges. While ongoing mitigation efforts aim to reduce pollutant concentrations, addressing the sources of pollution remains critical for achieving sustainable improvements. This study investigates the geographical origins and sectoral contributions of CO, NO2 and PM2.5 in Delhi. Pollutant concentration data and gridded meteorological data were utilized to identify pollution transport pathways and source regions, the study applied back trajectory analysis and the Concentration Weighted Trajectory method at 100 meters above ground level for winter, pre-monsoon, monsoon, post-monsoon season of year 2022. Airshed is delineated using back trajectory frequency analysis which extends across the Indo-Gangetic Plain, from Uttar Pradesh in India to Lahore in Pakistan. Using WRF-Chem model and emission inventories for 2022, pollutant contributions from six major sectors, namely agriculture, energy, industry, residential, transportation, and waste were estimated for 1st November 2022. Outputs were downscaled to a spatial resolution of 5 km for the regional airshed and 1 km for the local airshed. Sectoral contributions were found to be significantly higher than those estimated in earlier studies that were limited by administrative boundaries. The results show that dominant pollution sources vary by scale: for regional airshed, residential emissions contribute most to CO (55.9%), while in local airshed, transportation is the largest contributor (54.2%). Stubble burning contributed 18.01% to PM2.5, 1.43% to NO2, and 3.6% to CO in the regional airshed, higher than in the local airshed. These findings underscore the importance of adopting a regional airshed-based approach to air pollution management that considers pollutant sources beyond the administrative boundaries of Delhi. Keywords: Airshed- Source contribution- Stubble burning- Back trajectory- WRF-Chem Share Link | Plain Format | Corresponding Author (Prakriti .)

290	Topic D: Geospatial Data Integration	ABS-356
A Comprehensive Framework for Multisource Spatiotemporal Fusion of Optical and SAR Images for Flood Mapping in a Cloud-Based Environment Greetta Pinheiro (*a), Sonajharia Minz(b) a) Assistant Professor, Faculty of School of Computer Science Engineering & Technology, Bennett University, India *greetz.pinheiro[at]gmail.com b) Professor, Faculty of School of Computer and System Sciences, Jawaharlal Nehru University, India Abstract Floods are increasing in frequency and severity due to climate change, posing a major challenge to sustainable development. While satellite remote sensing is a powerful tool for disaster monitoring, relying on single sensors presents significant performance bottlenecks. Optical imagery is often obscured by the cloud cover and heavy rainfall typical of flood events, whereas Synthetic Aperture Radar (SAR) data, despite its all-weather capability, can suffer from over-classification of permanent water bodies, leading to inaccurate flood extent maps. This research proposes a novel and scalable framework for high-accuracy flood mapping by leveraging the complementary strengths of Sentinel-1 SAR and Sentinel-2 optical imagery within the Google Earth Engine (GEE) cloud platform. Our automated, two-stage methodology first employs a synergistic combination of a SAR change detection index and Edge Otsu segmentation on Sentinel-1 data for rapid initial flood detection. This is followed by a critical refinement stage that integrates the JRC Global Surface Water dataset to accurately mask permanent water bodies, thereby correcting over-classification errors and DEM-based slope and elevation masks to eliminate false positives. The entire workflow is designed for seamless implementation in GEE and subsequent integration into QGIS for local-level analysis and decision support. This framework provides an efficient, accessible, and precise tool for policymakers and first responders, contributing directly to the UN Sustainable Development Goal 13. Keywords: Disaster Resilience, Flood Mapping, Google Earth Engine, Multi-source Fusion, Sentinel-1, SDG 13 Share Link | Plain Format | Corresponding Author (Greetta Pinheiro)

291	Topic D: Geospatial Data Integration	ABS-102
Synergizing Point-Based CCTV and Wide-Area Remote Sensing Intelligence for Adaptive Flood Monitoring in Bandung Bayulodie Vallianto (a,b*), Masahiko Nagai (a), Yusuf Cahyadi (c) a) Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Japan b) National Research and Innovation Agency (BRIN), Jakarta, Indonesia c) Bandung Command Center, Bandung, Indonesia *bayulodie.val[at]gmail.com Abstract Urban flood monitoring remains a challenge for cities where rapid inundation during the rainy season disrupts transportation and risks public safety. This study addresses the critical gap in urban flood monitoring with the aid of combining real-time point-based closed-circuit television (CCTV) streams with wide-area remote sensing intelligence for Bandung City, Indonesia. Our framework continuously processes feeds from 10 flood-prone geotagged CCTV locations, extracting frames every 20 seconds through OpenCV and classifying water severity (dry, wet, or flood) using a pre-trained MobileNetV2 and fine-tuned on 3,058 actual frames that achieve promising performance with 94% classification accuracy in controlled tests. When floods are detected, the pipeline triggers elevation-guided spatial interpolation using a Digital Elevation Model (DEM), modeling flood spread along low-lying roads through Inverse Distance Weighting. This method estimates water surface elevation across a 500-meter radius around each flood point. The interpolated output traces probable inundation extent by comparing water elevations against ground-level DEM values, providing emergency responders with actionable flood spread forecasts. This integration strategically bridges the temporal gain of CCTV (real-time point data) and the spatial intelligence of remote sensing (DEM terrain analysis), overcoming their individual shortcomings. As an ongoing studies initiative, the framework is being refined for operational assessment by means of the Bandung Command Center, with future work focusing on field deployment. Keywords: Flood severity classification, Geospatial fusion, MobileNetV2, Real-time interpolation, Bandung Share Link | Plain Format | Corresponding Author (Bayulodie Vallianto)

292	Topic D: Geospatial Data Integration	ABS-103
Assessing Economic Benefit of Climate Adaptation for Urban Flooding in the Colombo Metropolitan Area, Sri Lanka Andi Besse Rimba1*, Ichiro Sato1, Naho Yoden2, Akiko Matsumura2, Daiju Narita3 and Daikichi Ogawada2 1 JICA Ogata Research Institute, Japan 2 Nippon Koei Co., Ltd, Japan 3 Graduate School of Arts and Sciences, University of Tokyo, Japan Abstract Flooding presents a growing challenge for rapidly urbanizing regions under intensifying climate change. This study assesses the economic benefits of climate adaptation through urban flood control in the Colombo Metropolitan Area, Sri Lanka. Using flood projections derived from five Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0 and SSP5-8.5), we integrate hydrodynamic modeling with high&#8209-resolution satellite imagery (10 m and 0.5 m) to map exposure and vulnerability of buildings across the Kalu Oya and Mudun Ela basins. Economic damage is estimated using structure-specific depth-damage functions for four building categories and compared under three protection levels: no measures, partial protection (1/25-year), and full protection (1/50-year). We found that full protection can reduce flood-related economic damage by approximately 40 - 71%, equivalent to avoiding losses of LKR 5 million to 5 billion , depending on the building type and scenario. The most significant benefits from full protection are gained wooden and unreinforced masonry (URM) houses. In contrast, commercial buildings and houses with concrete frames show smaller benefits (approximately 30-50%). Future climate scenarios affect risk, but structural vulnerability and protection level mainly determine how well we can adapt. These findings provide actionable evidence for urban planners and policymakers to prioritize cost&#8209-effective, risk&#8209-informed flood adaptation strategies in rapidly urbanizing coastal metropolitan areas. Keywords: Shared Socioeconomic Pathways (SSPs), Climate Modeling, Cost-Benefit Analysis, Disaster Risk Reduction, High-Resolution Satellite Imagery Share Link | Plain Format | Corresponding Author (Andi Besse Rimba)

293	Topic D: Geospatial Data Integration	ABS-104
Applying Google Earth Engine to Support Flood Policy Decisions in Makassar City Andi Besse Rimba 1,* , Andi Arumansawang 2 , I Putu Wira Utama 3,4 , Saroj Kumar Chapagain 5 , Made Nia Bunga 4,6, Geetha Mohan 7 , Kuncoro Teguh Setiawan 8 and Takahiro Osawa 9 1. JICA Ogata Research Institute, Shinjuku, Tokyo, Japan 2. Karya Alam Selaras Ltd., Citra Land, Jl. Talassa City Block 37 A Kapasa, Makassar 90245, Indonesia 3. Development Planning Agency of Bali Province, Jl. Cok Agung Tresna, Sumerta Kelod, Denpasar City 80239, Indonesia 4. Doctoral Program of Environmental Science, Udayana University, Jl. P.B. Sudirman Denpasar, Bali 80114, Indonesia 5. Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES), United Nations University, Ammonstrasse 74, 01067 Dresden, Germany 6. Fisheries Faculty, The University of 45 Mataram, Jl. Imam Bonjol No. 45 Cakranegara, Mataram City 83239, Indonesia 7. Global Research Centre for Advanced Sustainability Science (GRASS), University of Toyama, 3190 Gofuku, Toyama City 930-8555, Japan 8. Research Center for Remote Sensing, BRIN, Jl. Raya Jakarta Bogor Km 46, Cibinong, Bogor 16911, Indonesia 9. Center for Remote and Application of Satellite Remote Sensing, Yamaguchi University, 2-16-1 Tokiwadai, Ube 755-8611, Japan Abstract Makassar City frequently experiences monsoonal floods, typical of a tropical city in Indonesia. However, there is no high-accuracy flood map for flood inundation. Examining the flood inundation area would help to provide a suitable flood policy. Hence, the study utilizes multiple satellite data sources on a cloud-based platform, integrating the physical factors of a flood (i.e., land use data and digital elevation model-DEM-data) with the local government^s urban land use plan and existing drainage networks. The research aims to map the inundation area, identify the most vulnerable land cover, slope, and elevation, and assess the efficiency of Makassar^s drainage system and urban land use plan. The study reveals that an uncoordinated drainage system in the Tamalanrea, Biringkanaya, and Manggala sub-districts results in severe flooding, encompassing a total area of 35.28 km2 . The most affected land use type is cultivation land, constituting approximately 43.5% of the flooded area. Furthermore, 82.26% of the urban land use plan, covering 29.02 km2 , is submerged. It is imperative for the local government and stakeholders to prioritize the enhancement of drainage systems and urban land use plans, particularly in low-lying and densely populated regions. Keywords: flood policy- flood map- inundation map- Makassar City- Indonesia- urban land use planning- satellite imagery Share Link | Plain Format | Corresponding Author (Andi Besse Rimba)

294	Topic D: Geospatial Data Integration	ABS-360
Spatiotemporal Analysis of Shoreline Dynamics in Makassar, Indonesia (1970-2025) Using Multi-Temporal Satellite Imagery and Historical Map Safri Burhanuddina) b), Hansan Park b), Ilham Alimuddin a), Achil Samad c), Yazid Ridla b), &#8288-Indrawan Fadhil Pratyaksa b), Iqbal Maulana Cipta b), Dhiya Ulhaq Fathiyah b), Sandiaga Swahyu Kusuma b), Bau Ashary Nasir d) a) TG-FE Unhas, b) MTCRC, c), FIKP Unhas, d), KIOST Abstract Coastal zones are highly dynamic environments shaped by both natural processes and significant anthropogenic influences. In rapidly urbanizing areas like Makassar, Indonesia, monitoring shoreline changes is critical for sustainable coastal management. This study aims to provide a comprehensive spatio-temporal analysis of Makassar shoreline dynamics by integrating historical map data (1841,1861,1852,1897 and 1901) with a multi-temporal analysis of satellite imagery from 1970 to 2025. We utilized a time-series of Landsat imagery (Landsat 1-8) to extract annual shoreline positions. The methodology involved cloud-filtered pre-processing, calculation of water indices (NDWI, MNDWI), waterline extraction using Canny edge detection, and morphological processing to generate accurate shoreline vectors. The results reveal that Makassar^s shoreline is exceptionally dynamic, characterized primarily by massive land reclamation activities. Significant change is observed at the Makassar New Container Port and the Citra land reclamation area. Analysis indicates erosion occurring in North of the Jeneberang River estuary, posing a threat to the Tanjung Layar Putih Beach. This research quantifies five decades of natural and anthropogenically-driven coastal transformation in Makassar. These findings provide critical, evidence-based data to support sustainable coastal planning and management strategies, highlighting the profound impact of urban development on the coastal landscape. Keywords: Shoreline dynamics, remote sensing, GIS, Landsat, land reclamation, coastal management, Makassar Share Link | Plain Format | Corresponding Author (Safri Burhanuddin)

295	Topic D: Geospatial Data Integration	ABS-105
From Fixed to Automated Mirror Arrays: Advancing Multi-Satellite Image Registration Toward Operational Harmonization MUHAMMAD DANIEL IMAN BIN HUSSAIN (a*), MASAHIKO NAGAI (a), VAIBHAV KATIYAR (b) a) Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Japan *danieliman9897[at]gmail.com b) New Space Intelligence Inc., Japan Abstract Accurate alignment of satellite imagery across different sensors and spectral bands is critical for reliable multi-source remote sensing applications. Building on our previous work that identified a Fourier-based hybrid co-registration method-combining mirror arrays with local refinement-as the most effective strategy, this study focuses on extending the method to support high-accuracy registration across multiple satellites and imaging conditions. We evaluate a newly developed automated mirror array system that uses TLE-based satellite tracking and dynamic signal adjustment via variable mirror sizes to accommodate spatial resolutions from 15 m to 0.5 m. Compared to the previously used fixed mirror arrays, the automated system requires no manual setup or repositioning and ensures more consistent, real-time alignment with satellite overpasses. The hybrid co-registration method is applied to imagery from Sentinel-2, PlanetScope, and GRUS satellites, targeting both inter-satellite co-registration and intra-satellite band-to-band registration. We quantitatively assess improvements in registration accuracy using RMSE and CE90 metrics, and qualitatively validate results using the Global Reference Image (GRI). A practical case study is demonstrated to illustrate the impact of improved registration on downstream analysis, where misalignment between high and medium resolution images may lead to inaccurate change detection. Results show that the automated mirror array improves geometric consistency across sensors and enhances the interpretability of stacked multi-temporal data. The findings underscore the potential operational advantages of automated mirror arrays as scalable ground references for the harmonization of multi-satellite imagery, facilitating more accurate Earth observation and downstream applications such as environmental monitoring and urban analysis. This work also lays the foundation for future research into integrated radiometric calibration and automated calibration pipelines for emerging microsatellite constellations. Keywords: Satellite image registration, Mirror array, Multi-sensor harmonization, microsatellites Share Link | Plain Format | Corresponding Author (MUHAMMAD DANIEL IMAN BIN HUSSAIN)

296	Topic D: Geospatial Data Integration	ABS-106
Youth-Driven Participatory Mapping for Tourism: A Digital Approach for Promoting Cultural Heritage Noor Afiqah Mohd Nashar, Assoc Prof Dr Illyani binti Ibrahim International Islamic University Malaysia Abstract In the literature of tourist-based tourism, the significance of digital documents and promote local heritage such as interactive mapping has become significant. This study focused on the role of youth in developing interactive tourism maps using Geographical Information Systems (GIS) and participatory mapping methods. The project, which will be carried out in Sungai Lembing, Pahang will engage local youth in spatial and cultural data collection that comprises site coordinates, oral histories, photographs and environmental features. The outcomes arising are in the form of open source maps like Google My Maps and StoryMapJS using multimedia features like images, videos, and audio. A mixed-methods approach brings together qualitative such as interviews, observations with spatial mapping activities and digital literacy training. The initial findings show that youth involvement increases visibility of tourism resources and makes people more aware of sustainable tourism and cultural heritage. The study thus highlights the value of interactive community-based mapping to the development of youth-led digital tourism and support cultural resilience and local empowerment. Keywords: Geographic Information Systems (GIS), Digital Tourism, Participatory mapping Share Link | Plain Format | Corresponding Author (Noor Afiqah Mohd Nashar)

297	Topic D: Geospatial Data Integration	ABS-362
Analysis of Lightning Characteristics and Hazard Prediction in Karimun Regency Using Maximum Entropy (MaxEnt) Modeling Ilham Syarief Putra, Lalu Muhamad Jaelani, and Muhamad Sawal Class III Meteorological Station H.AS. Hanandjoeddin - Belitung, Meteorological, Climatological, and Geophysical Agency (BMKG), Indonesia Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Indonesia Class IV Meteorological Station Raja Haji Abdullah - Karimun, Meteorological, Climatological, and Geophysical Agency (BMKG), Indonesia Abstract This study analyzes the characteristics and predicts lightning hazard zones in Karimun Regency Indonesia using the Maximum Entropy MaxEnt model Lightning strike data from 2022 to 2024 comprising 82795 events across 12 sub-districts were analyzed alongside environmental variables including precipitation elevation and land cover The MaxEnt model demonstrated good performance with an Area Under the Curve AUC value of 0.777 and a regularized training gain of 0.457 The analysis revealed that land cover was the most influential variable 63.3% followed by precipitation 32.5% and elevation 4.2% Temporally lightning activity peaked in April with 17875 strikes coinciding with the transitional monsoon period Spatial distribution showed that approximately 60-65% of lightning strikes occurred in low-elevation areas 0-22 meters above sea level with scrubland and natural vegetation areas experiencing the highest frequency >63% Interestingly the correlation between monthly precipitation and lightning occurrences was weak R2 < 0.1 at most locations except in Kundur Utara District which exhibited a moderate negative correlation R2 = 0.340 Diurnally lightning activity was highest in the afternoon (13:00-18:00) with cloud-to-ground negative CG- strikes dominating 56-61% across all sub-districts The model successfully identified high-risk areas concentrated in open zones with high moisture content particularly over water bodies and agricultural lands These findings provide valuable insights for lightning risk mitigation strategies and early warning systems in tropical archipelagic regions emphasizing that land surface characteristics play a more significant role than precipitation amount in determining lightning occurrence patterns. Keywords: MaxEnt, Lightning Hazard, Karimun Regency, Lightning Cover, Elevation Share Link | Plain Format | Corresponding Author (Ilham Syarief Putra)

298	Topic D: Geospatial Data Integration	ABS-363
GIS-Based Multicriteria Site Selection for Flood Retention: A Case Study in Rembau, Negeri Sembilan, Malaysia Gs Haji Rosli bin Yusop, Dr Zamri bin Ismail Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia Abstract Urban flooding is a growing concern in Malaysia, particularly in vulnerable districts like Rembau, Negeri Sembilan. This study identifies optimal locations for flood retention ponds by integrating a Multi-Criteria Decision Analysis (MCDA) framework with Geographic Information System (GIS). Criteria considered include land use, slope, soil permeability, hydrological factors, proximity to river networks, and existing drainage infrastructure. The Analytic Hierarchy Process (AHP) was applied to derive weights for each parameter based on expert judgment and stakeholder input. Weighted overlay analysis generated suitability maps highlighting priority zones, especially in moderately sloped areas near natural waterways. Field validation confirmed site feasibility. Findings demonstrate the practical value of GIS-MCDA integration for flood mitigation and provide a replicable model for other flood-prone regions. Keywords: GIS-MCDA, Analytic Hierarchy Process, Flood Mitigation, Retention Pond, Spatial Planning Share Link | Plain Format | Corresponding Author (Rosli Yusop)

299	Topic D: Geospatial Data Integration	ABS-364
The implementation of Digital Transformation in Urban Housing Management: a case study in Purwakarta Regency, Indonesia Difnu Topan Yamaguchi University Abstract Urban housing development in Indonesia faces significant challenges in the management and provision of public infrastructure, facilities, and utilities (PSU). This study investigates the use of photogrammetry as a digital tool for PSU asset verification, focusing on two housing clusters in Purwakarta Regency, West Java. By employing techniques like orthomosaic imaging and area calculations with Agisoft Metashape and QGIS, the research identifies discrepancies between planned and actual PSU provisions. The findings highlight photogrammetry^s potential to improve technical verification processes in housing asset management. While it does not constitute a complete digital twin system, this approach marks a step toward digital transformation in local housing governance, enhancing data collection and monitoring capabilities. Keywords: Urban Housing, Public Facilities and Utilities (PSU), Photogrammetry, Digital Transformationubmit This Sample Abstract Share Link | Plain Format | Corresponding Author (Difnu Topan)

300	Topic D: Geospatial Data Integration	ABS-113
Depth Prediction-Based Enhancement of Feature Matching in Urban Drone Imagery Geonseok Lee (a), Junhee Youn (b), Kanghyeok Choi (c*) a) Program in Smart City Engineering, Inha University, Incheon 22212, Republic of Korea b) Department of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Tech, Gyeonggi-do 10223, Republic of Korea c) Department of Geoinfomatic Engineering, Inha University, Incheon 22212, Republic of Korea *cwsurgy[at]inha.ac.kr Abstract Recent drone imagery has been actively utilised across various domains, including disaster monitoring and geospatial information acquisition. In particular, its application has been expanding in urban areas for purposes such as urban monitoring and construction site management. To effectively leverage drone imagery, the role of feature matching algorithms, which identify accurate correspondences between images, is critically important. These algorithms serve as fundamental components in 3D reconstruction and trajectory estimation techniques, such as Structure from Motion (SfM) and Visual Simultaneous Localization and Mapping (Visual SLAM). However, complex urban environments, characterisedby densely clustered high-rise buildings, abrupt terrain variations, and repetitive structural patterns, significantly degrade the accuracy and robustness of conventional 2D-based feature matching algorithms. Even with the incorporation of outlier rejection techniques such as Random Sample Consensus (RANSAC), mismatches frequently occur, adversely affecting the precision of downstream tasks such as 3D modelling and localisation. To address these limitations, this study proposes a novel feature matching enhancement method by introducing 3D spatial constraints through the integration of deep learning-based depth estimation into existing feature matching pipelines. Experimental scenarios were designed to reflect real-world urban complexities, including dense built-up areas, waterfront ecological zones, and road intersections, with considerations for variations in elevation and lighting conditions. The proposed approach demonstrated significant improvements in matching precision and geometric consistency compared to traditional feature matching algorithms. This research presents a methodology for improving feature matching performance in complex urban environments, and it is anticipated to serve as a foundational technology for high-precision applications such as large-scale map generation and urban spatial analysis. Keywords: feature matching- drone imagery- depth prediction Share Link | Plain Format | Corresponding Author (Geonseok Lee)

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