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301	Topic D: Geospatial Data Integration	ABS-114
A Robust Structure-from-Motion Framework under Appearance Variation Jiwoo Kang (a), Kanghyeok Choi (b*) a) Department of Geoinformatic Engineering, Inha University, Incheon 22212, Republic of Korea b) Department of Geoinformatic Engineering, Inha University, Incheon 22212, Republic of Korea *cwsurgy[at]inha.ac.kr Abstract Structure from Motion (SfM) is a technique that reconstructs the three-dimensional (3D) structure of a scene from images captured at different viewpoints. Over the past few decades, it has been widely applied in various domains, including digital twins and virtual environments. Despite ongoing advancements, challenges still remain such as high computational costs in large-scale scenes and reduced performance under significant appearance variation. A typical SfM pipeline involves feature matching between pairs of images, followed by the iterative optimization of camera parameters and 3D points via bundle adjustment. In large-scale scenes, the number of image pairs and parameters to be estimated increases rapidly, leading to computational overhead. Furthermore, significant visual discrepancies between images can degrade feature matching performance, ultimately reducing the quality of reconstruction. Therefore, we propose a database-driven framework that enables robust and efficient localization under such conditions. The proposed framework consists of an offline map construction phase and an online localization phase. In the offline phase, accurate camera poses and 3D scene geometry are reconstructed using SfM, from which a database is built. In the online phase, given a query image captured at a different time, its pose is estimated by referencing the pre-built database. This approach not only enables accurate localization under appearance variation, but also reduces the number of parameters involved in bundle adjustment, thereby improving computational efficiency. The proposed framework enables efficient and accurate 3D localization even in large-scale scenes and considerable appearance variation. It can be effectively applied to a wide range of real-world applications, including the localization of dynamic objects such as pedestrians and the management of large-scale spatial information. Keywords: structure from motion- appearance variation- database-driven framework Share Link | Plain Format | Corresponding Author (Jiwoo Kang)

302	Topic D: Geospatial Data Integration	ABS-370
Challenging the Development of Rice Protection Area at The Nusantara New Capital of Indonesia (IKN) Baba Barus(a,b), La Ode S. Iman(b), Wahyu Iskandar(a,b*), Reni K. Tejo(a) a. Department of Soil Science and Land Resources, IPB b. Centre for Regional System Analysis, Planning and Development, GITIIA Program, LRI PSEK IPB *wahyuiskandar[at]apps.ipb.ac.id Abstract Developing food sources to meet at least 30 percent of the Ibu Kota Nusantara (IKN) population poses a real challenge. In the initial planning stage, approximately 14000 hectares of land were designated for paddy cultivation. However, competing land uses-including residential development, coal mining, and oil palm expansion-pose significant challenges to securing land for food production. To address this issue, this study assessed the potential areas that could be designated as protected paddy fields by employing geospatial methods. Specifically, the analysis integrated land suitability data for wet and dry paddy, LULC information, spatial planning, forest planning, and other supporting datasets. Moreover, a multi-criteria evaluation approach-incorporating scoring, weighting, and spatial selection-was applied to identify the most suitable areas. The results indicate that several regions are suitable for designation as protected food crop areas, which can be classified into four clusters: Sepaku-1, Sepaku-2, Samboja, and Muara Jawa. These clusters differ both in terms of biophysical suitability and the socio-economic characteristics of farmers. The analysis revealed that the existing wet paddy area is 2375 ha, while the existing dry paddy area is 1308 ha. In contrast, the potential land available for wet paddy is 8290 ha, and for dry paddy is 13642 ha. Consequently, the overall potential paddy area is greater than initially projected- nevertheless, the expansion of wet paddy fields is not feasible. Based on the general patterns of paddy land conversion, Samboja and Muara Jawa are recommended as priority regions for paddy protection and development. Keywords: paddy protection area, geospatial method, IKN, paddy suitability, land conversion Share Link | Plain Format | Corresponding Author (Wahyu Iskandar)

303	Topic D: Geospatial Data Integration	ABS-373
Analysis Of Slope Stability Level Using Kinematic Analysis Method And Slope Mass Rating Method At PT. MDA, Luwu Regency, South Sulawesi Province Auliya Aprilia (1), Prof. Dr. Ir.. Busthan Azikin, M.T (2), Ilham Alimuddin, S.T., M.Gis, Ph.D. (3), Dr. Eng. Hendra Pachri, S.T., M.Eng(4) 1Student, Faculty of Engineering, Hasanuddin University, Indonesia 2Lecturer, Faculty of Engineering, Hasanuddin University, Indonesia 3Lecturer, Faculty of Engineering, Hasanuddin University, Indonesia 4Lecturer, Faculty of Engineering, Hasanuddin University, Indonesia Abstract Slope stability is a crucial factor in open-pit mining operations, as it directly affects both operational efficiency and worker safety. Slope failures can lead to significant material losses and endanger human lives. This study aims to evaluate slope stability in the mining area of PT. MDA, located in Latimojong District, Luwu Regency, South Sulawesi Province, using the Kinematic Analysis and Slope Mass Rating (SMR) methods. The research methods include field mapping, slope geometry measurements, and discontinuity analysis using the scanline method. Rock mass parameters were evaluated using the Rock Mass Rating (RMR) classification system and subsequently modified by SMR correction factors. In addition, stereographic analysis was conducted to identify potential failure types, including planar, wedge, and toppling failures. The results show that the dominant lithology in the study area consists of igneous rocks such as gabbro and diorite porphyry, with RMR values ranging from 60 to 77, indicating fair to good rock mass quality. The SMR values reflect varying degrees of slope stability, from stable to unstable, depending on the orientation of discontinuities relative to the slope face. Recommended technical reinforcements include support systems such as bolting, shotcrete application, and drainage systems (toe ditch). In conclusion, the integrated application of Kinematic Analysis and SMR provides accurate slope stability assessments and serves as a reliable reference for geotechnical risk mitigation and slope design planning in mining operations. Keywords: Keywords: Slope Stability, Kinematic Analysis, Slope Mass Rating, Rock Mass Rating. Share Link | Plain Format | Corresponding Author (Auliya Aprilia)

304	Topic D: Geospatial Data Integration	ABS-374
Experience in development of smallholder rice field area using Goespatial thematic data with different scale in Merauke, South of Papua Indonesia Baba Barus (a,b), La Ode S. Iman (b), Utamy Sukmayu Saputri (c), Arien Heryansah (d), M. Yanuar J. Purwanto (b), Yuli Suharnoto (e), Nana Rostana(b), Aditya Yumansyah (b), Teguh M. Iskandar (b), Diar Shiddiq (b*) a. Department of Soil Sciences and Land Resources, IPB b. Centre for Regional System Analysis, Planning and Development, GITIIA Program, LRI PSEK IPB c. Nusa Putra University d. Ibnu Khaldun University e. Civil and Engineering IPB *diar.shiddiq[at]apps.ipb.ac.id Abstract The Indonesian government promotes food security through programs such as food estates, farmland optimization, and smallholder rice field development. This study reports experiences from Merauke, using geospatial thematic data of different scales for land suitability analysis. The process began with coarse-scale datasets for rapid zoning, followed by detailed data and field verification for accurate delineation. Results show major discrepancies between coarse and detailed analyses, underscoring risks of overestimation and spatial inaccuracy when relying solely on macro-level data. Three key constraints were identified: limited time, procedural inefficiency from repeated analyses, and lack of high-quality local data. The study recommends realistic timelines for data collection and validation, alignment of analytical steps with the availability of detailed data, and the design of rice field layouts based on optimal management units. While coarse data support initial screening, detailed geospatial information and ground truthing are essential for sustainable, conflict-free, and productive rice field development. Keywords: Smallholder Rice Fields, Geospatial Data, Multi-Scale Analysis, Land Suitability, Clean and Clear Land, Merauke, Food Security Share Link | Plain Format | Corresponding Author (Baba Barus)

305	Topic D: Geospatial Data Integration	ABS-119
Spatial Variation of Local Population in Inducement Areas under Urban Shrinkage Ayano Shiraishi(a*),Kichiro Kumagai(b*) a) Division of Social Development Engineering, Graduate school of Science and Engineering, Setsunan University b) Dept. of Civil & Environmental Engineering, Setsunan University Abstract In Japan, the population peaked in 2008 and is now declining, with low birthrates and an ageing population. In addition to causing a reduction in society^s willingness to develop, such a population decline makes it difficult to maintain the quality of public services and may lead to further urban decline. The Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) has been promoting measures for Location Optimization Plan, which aims to direct residential and urban functions to specific areas to increase urban sustainability. However, ^urban spongification^, the random occurrence of vacant houses and lots in city centers, has been identified as an issue. The degradation of local communities and the deterioration of public safety and urban landscapes due to urban spongification may hinder the concentration of the population. The continuous monitoring of urban structure is required as a means of addressing urban spongification. We have focused on the spatial distribution of the local population and developed a method to statistically define the areas where the population density is locally low. By iterating the calculation of the size of a locally low-density population area and visualizing the variation over multiple time periods, we were able to gain a detailed understanding of the variation in the spatial distribution of the local population. On the other hand, the characteristics of the local population distribution in the inducement areas have not been determined from the point of view of the analysis of urban spongification. In this study, we analyzed the characteristics of spatial variation in the local population distribution through a comparison with land use status, using a method developed by the authors. Keywords: local population dynamics, population decline, densely populated area, spatial autocorrelatio Share Link | Plain Format | Corresponding Author (Ayano Shiraishi)

306	Topic D: Geospatial Data Integration	ABS-376
Morphotectonic Evaluation and Land Surface Deformation Changes in the Western Segment of the Lembang Fault Silmi Afina Aliyan1*, Totok Doyo Pamungkas2 Fikri Algifahri1 Rafi Adyatma1 Melani Kartika Sari1 1Geographic Information Science, Faculty of Social Science Education, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi No.229, Kec. Sukasari 40154, Kota Bandung, Jawa Barat, Indonesia 2Geography Education, FPIPS UPI, Faculty of Social Science Education, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudi No.229, Kec. Sukasari 40154, Kota Bandung, Jawa Barat, Indonesia Abstract The Lembang Fault in West Java is an active horizontal fault that can cause surface deformation, especially in the western segment of the Cisarua region. The lack of information to the public increases the risk of casualties, so mitigation is needed through deformation monitoring. This study aims to detect and analyze deformation in Cisarua District using active remote sensing technology, specifically DinSAR based on Sentinel-1 imagery (2017 and 2025). Data is processed through pre-processing, filtering, and correction stages, followed by deformation analysis (phase conversion, pattern mapping, classification). The results are used to link deformation with fault activity, identify potential geological hazards, and analyze deformation dynamics temporally. The results show that land deformation in the Western Segment of the Lembang Fault for the period 2017-2025 shows active dynamics with varying uplift and subsidence patterns, where 2017 is characterized by contrasting deformation and 2025 is dominated by broad uplift. DInSAR technology has proven effective in detecting millimeter-scale displacements and mapping the spatial distribution of deformation that is closely correlated with earthquake events. The spatial and temporal patterns in Cisarua District confirm that this area is experiencing dynamic deformation and has a high level of geological vulnerability, thus requiring continuous monitoring. Keywords: Deformation, Lembang Fault, Remote Sensing, Sentinel-1a, DInSAR Share Link | Plain Format | Corresponding Author (Silmi Afina Aliyan)

307	Topic D: Geospatial Data Integration	ABS-125
FAIR perspective towards the development of Remote Sensing Imagery Metadata: A Comparative Study Using ISO 19115-1 and ISO 19115-2 Ting-Yu Chang(a*), Jung-Hong Hong(b) a)Graduate Student, Department of Geomatics, National Cheng Kung University, Tainan, Taiwan *tingyu217kk[at]gmail.com b)Professor, Department of Geomatics, National Cheng Kung University, Tainan, Taiwan Abstract The FAIR (Findable, Accessible, Interoperable, Reusable) principles are increasingly recognized as essential components in the dissemination and application of scientific data across various professional fields. Remote sensing imagery is crucial for environmental monitoring, as it provides vital spatiotemporal data. However, the degree to which the inherent characteristics of remote sensing data can adhere to the FAIR principles necessitates further exploration. Specifically, achieving interoperability and reusability hinges on the availability of metadata that comprehensively conveys information regarding acquisition context, sensor specifications, spatial frameworks, and processing lineage. This study investigates how the established schema of ISO 19115-1 and its imagery extension, ISO 19115-2, can facilitate the creation of metadata structures that align with FAIR principles for remote sensing images. Through an analysis of metadata standards mapping, we evaluate whether the selected metadata elements offer a structured and machine-actionable framework for detailing image-specific attributes. We assess their effectiveness in promoting semantic clarity, long-term consistency, and platform-independent integration, with a particular focus on enhancing interoperability and reusability across diverse systems and applications. Additionally, we discuss how the proposed metadata can improve the interoperable use of remote sensing images in environmental monitoring and change detection efforts. Our findings underscore the significance of metadata standards not only as documentation tools but also as facilitators of FAIR data governance. By highlighting the importance of interoperability and reusability over time and across platforms, this study contributes to the development of transparent, standardized, and application-ready geospatial data infrastructures. Keywords: FAIR Principles, Remote Sensing Imagery, ISO 19115 Share Link | Plain Format | Corresponding Author (TING YU CHANG)

308	Topic D: Geospatial Data Integration	ABS-126
Optimizing Groundwater Conditioning Parameters for Groundwater Potential Mapping Using Machine Learning Approaches in Klang and Langat River Basin Syarifah Raihana Syed Zabidi (a), Sharifah Norashikin Bohari (a*), Rizauddin Saian (b), Rohayu Haron Narashid (a) (a) Faculty of Built Environment, Surveying Science and Geomatics Studies, Universiti Teknologi MARA, Perlis Branch, Arau Campus, Malaysia *ashikin10[at]uitm.edu.my (b) Faculty of Computer and Mathematical Sciences, Surveying Science and Geomatics Studies, Universiti Teknologi MARA, Perlis Branch, Arau Campus, Malaysia Abstract Groundwater potential (GWP) studies relied heavily on the appropriate selection of parameters. Past studies have considered factors such as topography, hydrology, geology, land cover and climate changes- however, not all variables contribute equally to groundwater occurrence. Therefore, the proper selection of parameters is essential to ensure the accuracy and reliability of GWP prediction. This study aims to optimize 20 GWP conditioning parameters by utilizing several statistical approaches: correlation matrix, multicollinearity and chi-square tests. These parameters represents various factors, including topography, hydrogeology, land cover and also climate changes. The correlation analysis and multicollinearity test results indicate all parameters fall within the required thresholds, showing minimal redundancy and no multicollinearity issues. In contrast, the results from chi-square test indicate that 9 parameters: lineament density, elevation, geology, soil, slope, distance to fault, LULC, NDVI and drainage density exhibit significant contribution (p-value<0.05) and therefore are retained for GWP prediction. These optimized parameters were then applied to predict GWP areas in the Klang and Langat River basins using the random forest (RF) machine learning technique. 564 tubewell points were divided into 70% for training and 30% for testing. The results found that the highest groundwater potential areas were located in the central part of the basins, with a percentage of 14.02%. In contrast, the lowest groundwater potential areas were located in the northern and northeastern areas, with the percentages of 20.58%. The evaluation indicates the model exhibited strong performance, achieving an area under the curve (AUC) value of 0.927 for training and 0.860 for testing. The findings of this study will enhance the accuracy and reliability of groundwater potential mapping that can be used for the future sustainable groundwater management systems. Keywords: Groundwater potential identification- Optimization- Machine learning- Random Forest Share Link | Plain Format | Corresponding Author (Syarifah Raihana Syed Zabidi)

309	Topic D: Geospatial Data Integration	ABS-129
A 3D GIS Perspective Towards the Occlusion Problem in XR Environment Hao-Han Chang (a*), Jung-Hong Hong (b) a) Graduate Student, Department of Geomatics, National Cheng Kung University, Tainan, Taiwan *p66134072[at]gs.ncku.edu.tw b) Professor, Department of Geomatics, National Cheng Kung University, Tainan, Taiwan Abstract Extended Reality (XR) represents the convergence of physical and virtual environments, thereby creating a new operational framework. Within this context, Virtual Reality (VR) and Augmented Reality (AR) offer unique perspectives that enable innovative applications. VR is primarily concerned with delivering a fully immersive experience within a completely virtual setting, whereas AR focuses on the concurrent presentation of virtual elements alongside the physical world. A significant challenge encountered in XR environments is the ^occlusion problem,^ which negatively impacts spatial realism and depth perception when users engage with the virtual environment. The challenges associated with VR and AR applications, however, differ considerably. VR applications prioritize the completeness of phenomena and the accuracy of spatial representation, while AR applications must also account for the spatial relationships between virtual information, real-world objects, and users. Inadequate management of occlusion can result in the erroneous rendering of virtual information in front of real objects. This research aims to investigate the occlusion problem within XR environments by employing 3D Geographic Information System (GIS) data as a reference for constructing models in the virtual domain. By harnessing the benefits of multi-dimensional spatial representation and precise positioning, along with rendering techniques from computer graphics, the study aspires to establish an accurate visual relationship between occluders and occluded objects. The investigation begins with an analysis of observable phenomena from the user^s perspective, contrasting the considerations necessary for the distinct development of VR and AR simulated environments, and proposes strategies to alleviate the occlusion problem through the inclusion of 3D GIS data. A campus environment has been selected as a practical case study, which includes a web-based interactive platform that integrates XR technology. This initiative not only demonstrates the efficacy of system integration and application but also explores its potential advantages and the challenges related to practical implementation. Keywords: Extended Reality, Virtual Reality, Augmented Reality, Geographic Information Systems, Occlusion Problem Share Link | Plain Format | Corresponding Author (HAO HAN CHANG)

310	Topic D: Geospatial Data Integration	ABS-140
Range-based Scan Matching for LiDAR-SLAM with Non-repetitive Omnidirectional LiDAR Masafumi Nakagawa(a*), Kenshiro Yamamoto(a), Tetsu Yamaguchi(a), Nobuaki Kubo(b), Etsuro Shimizu(b) a) Shibaura Institute of Technology, Japan *mnaka[at]shibaura-it.ac.jp b) Tokyo University of Marine Science and Technology, Japan Abstract There are two main types of omnidirectional 3D LiDAR. The first type is a horizontally scanning LiDAR that performs repetitive linear scans. The second type uses micro-electro mechanical system mirrors to produce non-repetitive, non-horizontal scan lines, enabling the acquisition of wide field-of-view point clouds. Although repetitive-scanning omnidirectional LiDAR can capture high resolution data across a wide horizontal range, it is limited by a narrow vertical field-of-view and low vertical angular resolution. To achieve uniform spatial resolution for LiDAR-SLAM applications, multiple LiDAR sensors are often be combined or the LiDAR is physically rotated. Conventional LiDAR-SLAM techniques typically rely on repetitive scanning LiDAR and apply scan matching algorithms, such as the iterative closest point methodology, to align sequential scan lines. These methodologies benefit from the presence of numerous candidate correspondences between adjacent scans and employ point-to-point, point-to-line, or point-to-plane matching strategies. In contrast, although it requires a longer acquisition time, a non-repetitive and omnidirectional LiDAR achieves high angular resolution in both the horizontal and vertical directions. Consequently, a non-repetitive and omnidirectional LiDAR can reduce the number of 3D-LiDAR units needed and omit the need for rotational mechanisms when capturing wide-area point clouds. However, conventional scan-matching techniques are less effective for SLAM with non-repetitive LiDARs because the adjacent scans have few overlaps. Existing approaches address this limitation by incorporating motion distortion correction using inertial measurement units for tightly coupled position and attitude estimation. These approaches also use scan matching via point-to-plane methods based on multiple surface normals. Additionally, scan-to-map alignment registers local scans with global 3D maps. This study investigates the use of a non-repetitive, omnidirectional LiDAR for SLAM applications to develop a more compact and efficient LiDAR-SLAM system. To enable robust SLAM processing in environments lacking planar surfaces, we propose an range-based point-to-point scan matching methodologt tailored to non-repetitive omnidirectional LiDAR. We verified the feasibility of the proposed methodology using data acquired from a non-repetitive omnidirectional LiDAR mounted on a boat. Keywords: LiDAR-SLAM, scan matching, omnidirectional LiDAR, autonomus boat Share Link | Plain Format | Corresponding Author (Masafumi Nakagawa)

311	Topic D: Geospatial Data Integration	ABS-146
Uncertainty quantification and spatial downscaling for passive microwave remote sensing of soil moisture Jingyao Zheng, Tianjie Zhao State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences Abstract High-resolution soil moisture (SM) data are essential for hydrological and climate applications but limited by the coarse resolution of passive microwave SM products. This study conducted the first systematic evaluation of 24 global SM products using ground networks. SMAP passive microwave SM demonstrated superior accuracy (ubRMSD < 0.04m^{3}/m^{3}), identifying it as the optimal input for downscaling. Key error sources include vegetation parameter uncertainty (causing dry bias) and limitations in existing freeze/thaw products (leading to over-filtering of valid SM retrievals). Validation method analysis revealed Triple Collocation (TCA) tends to underestimate errors, while Categorical TCA (CTC) generally ranks products correctly but is vulnerable when products covary. We rigorously assessed combinations of downscaling factors and methods. The NSDSI-2 factor (based on soil reflectance) combined with a Taylor series expansion method yielded the highest accuracy downscaled SM. Furthermore, we demonstrated that the evapotranspiration-based DISPATCH method enhances SM representativeness in humid zones specifically under conditions of sufficient evaporative demand (e.g., summer/dry pixels), explaining its limitations in energy-limited regions.Addressing the critical data gap, we produced a novel 1 km resolution downscaled SM product for the TP (2017-2020) using the optimal NSDSI-2 + Taylor approach. Extensive validation across five diverse TP networks confirmed this new product outperforms the original SMAP and other publicly available downscaled products in spatial/spatiotemporal metrics, despite showing limited improvement in densely vegetated areas. Keywords: Soil moisture- Passive microwave remote sensing- Accuracy evaluation- Spatial downscaling- Observation error- 1 km downscaled soil moisture products Share Link | Plain Format | Corresponding Author (Jingyao Zheng)

312	Topic D: Geospatial Data Integration	ABS-147
Pose Correction for SfM-based River Mapping using the Fixed Baseline and Optical Axis of an Omnidirectional Camera as Constraints Teruhiko Meguro (a*), Tetsu Yamaguchi (a), Nobuaki Kubo (b), Etsuro Shimizu (b), Masafumi Nakagawa (a) a) Shibaura Institute of technology, Japan *ah20092[at]shibaura-it.ac.jp b) Tokyo University of Marine Science and Technology, Japan Abstract In recent years, the acquisition of high-density point cloud data has been promoted as a way to create digital twins of cities. However, data preparation for urban river spaces has not advanced sufficiently. The two main methods of acquiring 3D data in these environments are LiDAR and image-based 3D measurement. The LiDAR method has the advantage of directly measuring distances and acquiring dense point clouds. However, the accuracy of point cloud acquisition depends heavily on exterior orientation estimation, which requires expensive GNSS/IMU systems. On the other hand, image-based point cloud generation primarily uses structure from motion (SfM) and multi-view stereo (MVS). Although SfM/MVS typically requires more time to generate point clouds compared to LiDAR, it enables the construction of more affordable measurement systems. However, matching large-scale image datasets involves high computational costs. In particular, the linear trajectories required for boat-based measurements along urban riverbanks pose challenges. Long measurement paths can lead to accumulated matching errors and distortions in the point cloud. To address this, we propose a method to improve the robustness of SfM-based pose estimation using omnidirectional images captured from a boat. First, mask images are generated to exclude water surface and occlusions, thereby enhancing image matching reliability. Then, constraints are introduced into the SfM process by leveraging the fixed baseline and optical axis alignment of the omnidirectional camera. These constraints stabilize image pair selection and pose estimation by guiding the image matching process with prior knowledge of camera arrangement. Finally, point clouds are generated using SfM/MVS, with reduced matching errors along the measurement trajectory. We also compared these results with those from LiDAR-SLAM and conventional methods. Keywords: structure from motion- multiview stereo- waterborne MMS- omnidirectional camera- pose estimation correction Share Link | Plain Format | Corresponding Author (Teruhiko Meguro)

313	Topic D: Geospatial Data Integration	ABS-150
Towards Effective LiDAR-Based Mapping with Handheld Rotating Sensors in Complex Environments Juyeon Lee (a), Kanghyeock Choi (b*) a) Program in Smart City Engineering, Inha University, Incheon 22212, Republic of Korea b) Department of Geoinformatic Engineering, Inha University, Incheon 22212, Republic of Korea *cwsurgy[at]inha.ac.kr Abstract Mobile LiDAR systems provide 3D mapping in complex environments with varied terrain and structures. To overcome the limited field of view of low-resolution LiDAR sensors, additional mechanical rotation is introduced to increase spatial coverage. However, this approach creates challenging conditions with both sparse point distributions and reduced frame-to-frame overlap, posing significant challenges for conventional SLAM algorithms in maintaining reliable scan matching and mapping consistency. This study identifies optimal SLAM algorithms for these conditions and enhances their performance through systematic evaluation. We collected point cloud data using a manually rotated Velodyne VLP-16 system in diverse environments including structured indoor spaces and open outdoor areas, relying solely on LiDAR data without GPS, IMU, or camera assistance. Our evaluation compares multiple existing SLAM algorithms to identify which methods demonstrate the highest robustness and mapping consistency when processing sparse, low overlap point cloud sequences. Beyond algorithm comparison, we explored adaptive strategies to enhance performance based on the characteristics of rotating sensors and varying environmental conditions. These strategies include data handling and algorithm configuration techniques specifically designed to address the challenges posed by sparse and irregularly sampled point clouds. The objective is to establish optimal integration strategies that facilitate stable LiDAR data alignment and consistent 3D mapping results. This work contributes to the practical deployment of compact rotating LiDAR systems for LiDAR-only 3D spatial modeling in GPS-denied environments. Keywords: handheld LiDAR- rotating sensor- SLAM Share Link | Plain Format | Corresponding Author (Juyeon Lee)

314	Topic D: Geospatial Data Integration	ABS-157
Stereo Image-based Relocalization for Robust Visual Odometry Yusuke Eshima(a*), Masafumi Nakagawa(a) a) Shibaura Institute of Technology, Japan ah20034[at]shibaura-it.ac.jp b) Tokyo University of Marine Science and Technology, Japan Abstract Mobile mapping systems (MMS) and unmanned aerial vehicles (UAVs) are widely used to quickly and safely collect 3D data for inspecting infrastructure, such as bridges, dams, roads, and railroads. One technical challenge is that self-position estimation by visual odometry is not easy when images are blurred due to camera movement and rotation. Therefore, we focused on visual odometry issues when mounting autonomous mobile robots such as indoor flying UAVs. Conventional research in the field of seamless indoor-outdoor UAV navigation has focused on visual simultaneous localization and mapping (Visual SLAM) integrated with Robot Operating System (ROS), as well as the 2D modeling and orthoimage generation of complex structures using UAVs equipped with OpenREALM. Studies utilizing ROS-based frameworks have typically conducted comparative analyses of sensing modalities, including LiDAR, monocular RGB cameras, and stereo camera systems. In our previous work, we developed flight control algorithms designed specifically for UAV-based infrastructure inspection tasks. Additionally, we proposed a method for enhancing the stability of Visual Odometry by incorporating multi-directional inertial measurements in conjunction with stereo imagery. Furthermore, we introduced a seamless positioning approach that enables seamless transitions between visual odometry and RTK-GNSS modes, thereby ensuring continuous and reliable localization in both GNSS-available and GNSS-denied environments. However, technical issues with visual odometry include the difficulty estimating self-position when images are blurred during camera movement and rotation. Therefore, we proposed a methodology that identifies visual odometry errors and directs users to the restarted position. Our methodology also restarts visual odometry using image matching on a sequence of images. Keywords: visual odometry, image matching, motion blur, odometry reinitialization Share Link | Plain Format | Corresponding Author (Yusuke Eshima)

315	Topic D: Geospatial Data Integration	ABS-161
GIS Characterization For Peatland Forest Fire Index Using GeoAI Technique Nadia Fitriana Rahimi, Fareesha Irdeena Azman, Aduwati Sali, Sheriza Mohd Razali Universiti Putra Malaysia Abstract Peatland forests are highly fire sensitive ecosystems, particularly during prolonged drought conditions. Incorporating Groundwater Level, GWL into Fire Weather Index, FWI system is crucial as GWL significantly influences fire risk. when GWL decreases, peat soil becomes increasingly exposed to the oxygen which raising its flammability and the likelihood of fire outbreaks. To enhance the fire risk prediction, the study proposes an improved method over the traditional Inverse Distance Weighting, IDW approach. Specifically, it integrates Geospatial Artificial Intelligence, GeoAI with Geographic Information System, GIS technologies for fire risk assessment and geospatial mapping. The paper explores the application of GeoAI in predicting GWL using both machine learning and deep learning techniques. Meteorological and spatial parameters such as temperature, rainfall, soil moisture, latitude, and longitude were used to increase the reliability of GWL predictions. The methodology includes data preparation, feature identification, model development and spatial mapping of actual versus predicted GWL values. Model performance was evaluated using RMSE, MAE, R2 score. Among the tested models, Long Short Term Memory, LSTM demonstrated the highest accuracy and lowest error and effectively capturing both temporal and spatial environmental patterns. predicted GWL values were taken then spatially mapped to visualize groundwater conditions and assess fire prone zones. The findings highlight the potential of advanced predictive models to enhance environmental monitoring, thereby strengthening efforts towards ecological sustainability and resilience to form a strong basis for future research on real time sensor integration and geospatial fire risk analysis. Keywords: GIS, IoT system, Machine learning, Peatland, Remote sensing Share Link | Plain Format | Corresponding Author (Fareesha Irdeena Azman)

316	Topic D: Geospatial Data Integration	ABS-173
Multiscenario Slope Stability Back-Analysis in Open-Pit Mining: A Case Study from Tanjung Enim, South Sumatra Goestyananda Pratama (a), Imam Achmad Sadisun (a*) Indra Andra Dinata (a) Muhamad Raihan (a,b) a) Geological Engineering Program, Bandung Institute of Technology Jalan Ganesha 10, Bandung 40132, Indonesia b) Natural Science Foundation Program, Ehime University, Ehime, Japan *iasadisun[at]itb.ac.id Abstract The landslide event on March 5, 2025, at 09:15 WIB was triggered by high-intensity rainfall lasting 5.95 hours, with a daily accumulation of 95.16 mm/day. The initial slope analysis showed a stable Factor of Safety (FoS) of 1.46 using the Morgenstern-Price method. However, since a landslide occurred in reality, a back analysis of slope stability was conducted to evaluate the failure mechanisms and contributing geotechnical parameters through engineering geological assessment, slope geometry modeling, and numerical simulations using Slide2 software. The Morgenstern-Price method was applied under various hydrological and dynamic loading scenarios to obtain the critical FoS value of the slope close to the actual landslide event. The 30-day cumulative rainfall infiltration scenario significantly reduced the FoS to 1.01 due to increased saturation in the middle and lower slope zones. The most critical condition occurred under the combined influence of haul load, seismic load, and rainfall infiltration, resulting in the lowest FoS of 0.99. These results indicate that the landslide was primarily triggered by extreme rainfall, which reduced the FoS below the slope stability threshold (FoS > 1.0) and did not meet the standards of SNI 8460:2017 and the Decree of the Minister of Energy and Mineral Resources of the Republic of Indonesia No. 1827K/30/MEM/2018, which require FoS < 1.1 for overall slope stability. Keywords: Slope stability- back-analysis- open-pit mining- rainfall infiltration- dynamic loading Share Link | Plain Format | Corresponding Author (Goestyananda Pratama)

317	Topic D: Geospatial Data Integration	ABS-174
A Conceptual Geospatial Data Management Model for National Parks: A Case Study of Sarawak Zakri Tarmidi, Noordyana Hassan, Suzanna Noor Azmy, Nurul Nadiah Yahya Universiti Teknologi Malaysia Abstract Effective and integrated geospatial information management plays a critical role in enhancing the administration, natural resource conservation, and sustainable tourism development of national parks. This study proposes a conceptual model for comprehensive geospatial data management, with a specific focus on national parks in Sarawak, Malaysia. The model comprises six core components: core geospatial data, park management information, tourism and activity data, socio-economic and community profiles, GIS infrastructure and technology, and data governance. By integrating these elements, the model aims to support data-driven decision-making, strengthen park administration, and facilitate information sharing among agencies and local communities. The model emphasizes the adoption of spatial data standards, the implementation of digital platforms such as WebGIS, and the active involvement of local communities in data collection and updates. It also addresses the need for multi-layered spatial analysis involving elevation models, buffer zones, and zoning of park attractions, infrastructure, and biodiversity hotspots. The conceptual model developed in this study serves as a foundational framework for establishing a transparent, efficient, and sustainable geospatial information system to support conservation and ecotourism planning in Sarawak^s national parks. Keywords: Spatial Data Management, SDI, Spatial Data Sharing Share Link | Plain Format | Corresponding Author (Zakri Tarmidi)

318	Topic D: Geospatial Data Integration	ABS-185
CityGML-based Representation of Urban River Environments using Waterborne MMS Point Cloulds with LOD-Aware Modeling Kantaro Kanai(a*), Tetsu Yamaguchi(a), Nobuaki Kubo(b), Etsuro Shimizu(b),Masafumi Nakagawa(a) a) Shibaura Institute of Technology, Japan *ah20005[at]shibaura-it.ac.jp b) Tokyo University of Marine Science and Technology, Japan Abstract CityGML is open data in XML format that can be used for various purposes, such as urban planning and disaster simulations, in the form of a 3D urban model. CityGML uses the concept of LOD (level of detail) to enable the integrated management of map data at different scales. However, there is a problem that LOD design and schema considerations for modeling landforms are insufficient in urban river environments. Additionally, traditional surveying methods such as aerial photogrammetry, mobile mapping systems (MMS), and ground-based laser surveying often encounter areas where observation is difficult. The current version of PLATEAU has not sufficiently advanced the development of, high resolution 3D map data for river environments. This study focuses on the 3D modeling of urban river environments. The goal is to promote the effective use of urban rivers by describing them with CityGML. One consideration is autonomous boat navigation technology, which requires precise GNSS/non-GNSS seamless positioning technology, autonomous control technology, and 3D river maps. Therefore, this study aims to create 3D maps for autonomous boats in urban rivers. To achieve this goal, we acquired point clouds from waterborne MMS, developed methods for assigning attributes to objects in urban river environments, and modeled them based on LOD levels. We also, investigated methods for describing the generated 3D models using CityGML. The 3D model can be used as maps for autonomous boats and are expected to support in the maintenance and management of river infrastructure. Keywords: Water-borne mobile mapping, CityGML, 3D modeling, LOD Share Link | Plain Format | Corresponding Author (Kantaro Kanai)

319	Topic D: Geospatial Data Integration	ABS-205
Urban Heat Exposure and Access to Cooling: A Network-based Analysis for Urban Green Space Accessibility in the Colombo Metro Region Area. Jayathilaka H. B.T.P1*, Ranasinghe D.A.S.M.2, Shiyan Zhai3, and Somasekara J.P.S.4 1College of Geographical Sciences: Reading PhD, Henan University, China 2Geography Information System Division: Assistan Director (Town Planning), Urban Development Authority, Sri Lanka 3College of Geographical Sciences: Professor, Henan University, China 4 Geography Information System Division: Director, Urban Development Authority, Sri Lanka Abstract Rapid urbanization, coupled with infrastructure development, reduction and fragmentation of green spaces, increases urban heat islands. Urban heat (UH) poses a threat to public health, and urban green spaces (UGS) benefit the well-being of residents while supporting sustainable urbanization and climate regulation services. Studying accessibility is a complex and dynamic process, and a gap remains in urban planning and accessibility studies regarding UGS, especially in developing countries. By addressing these gaps, this study focused on assessing UH distribution and UGS accessibility and identifying functionally beneficial areas in the Colombo Metro Region, which is the urban core of Sri Lanka. This study utilized secondary data, including Landsat images, land use, road networks, green infrastructure, and building data, and integrated them with remote sensing and GIS-based accessibility analysis. To determine which cooling areas are truly accessible to residents, appropriate accessibility metrics are applied. A service area analysis is then conducted, using buffer zones of 500 m, 1000 m, and 1500 m, to identify the areas served by the accessible cooling locations. The results reveal that main UGSs such as Angoda, Malabe Thangama, and Thalangama provide cooling services to Battamulla and Kaduwela, while heat-stressed zones, including Grandpass, Dehiwala-Mount Lavinia, Ratmalana, Maharagama, and Borella, remain underserved. There are 15% of residential buildings in high UH zones that fall within 500 m of accessible UGS, highlighting that 85% of heat-affected residents lack easy access to cooling spaces. It shows a critical cooling service gap in climate resilience infrastructure and an urgent need for heat vulnerability and equitable UGS access in urban planning. Developing green parks, introducing UH resilience zones, and supporting community-led cooling initiatives are highly recommended for this area, while providing actionable insights for urban planners and policymakers to encourage equitable access to UGS and improve climate resilience in urban environments. Keywords: Urban green spaces, urban heat, network analysis, accessibility Share Link | Plain Format | Corresponding Author (H. B. T. Prathibani Jayathilaka)

320	Topic D: Geospatial Data Integration	ABS-212
Mapping Landslide Vulnerability using Spatial Analysis of Weight of Evidences (WoE) in Central Mamuju, West Sulawesi Province, Indonesia Ilham Alimuddin (ab*), Muhammad Ichsan(b), Cahyadi Ramadhani(c) (a)Disaster Study Center of Hasanuddin University Kampus Unhas, Jalan Perintis Kemerdekaan Km 10, Tamalarea *ialimuddin[at]gmail.com (b)Department of Geological Engineering (c)Disaster Study Center of Hasanuddin University Abstract Mamuju Tengah Regency, West Sulawesi Province, is an area with a relatively high level of geological disaster risk, particularly landslides. Over the past five years, there have been several significant landslide incidents that have not only caused infrastructure damage but have also disrupted social and economic activities of the community. Spatial analysis to identify locations and map areas with high risk/hazard levels can be one of the efforts to assist in anticipating such disasters by serving as a basis for decision-making. The multivariate statistical method known as Weight of Evidences can be used to evaluate the influence of each factor on landslide hazards by overlaying landslide distribution data and then comparing it separately with various thematic data layers from eleven factors, including slope direction, elevation, distance from roads, distance from the river, slope gradient, geology, peak ground acceleration, distance from geological structures, soil type, land type, and land cover. The validation results shown in the Central Mamuju area are presented in the form of attribute tables and prediction level graphs, namely AUC (Area under Curve), which is one type of accuracy statistic for prediction models (probability) in assessing/analyzing the level of landslide vulnerability in the Central Mamuju area, which is 0.9303 (Excellent Model). It is known that the three most influential parameters in landslide events in this area are slope gradient, slope direction, and land cover. Keywords: Landslide Vulnerability, Weight of Evidence (WoE), Central Mamuju Share Link | Plain Format | Corresponding Author (Ilham Alimuddin)

321	Topic D: Geospatial Data Integration	ABS-225
Interpretable Machine Learning for Crash Severity Analysis of Food Delivery Motorcyclists I Gede Brawiswa Putra*, Febrian Fitryanik Susanta, Bimo Harya Tedjo, Pei-Fen Kuo Department of Geomatics, National Cheng Kung University, Taiwan, *10903014[at]gs.ncku.edu.tw Abstract The COVID-19 pandemic has significantly increased the demand for online food delivery services. This surge has intensified competition among platforms and placed greater pressure on delivery riders to prioritize speed over safety. As a result, crash incidents involving food delivery motorcycles have nearly doubled compared to those used for routine commuting. While previous studies have focused on general motorcycle crashes, few have examined the specific factors influencing crash severity among delivery riders. Additionally, most existing research relies on traditional spatial models, which may fail to capture nonlinear relationships and spatial heterogeneity. Another challenge is the imbalance in severity data, with serious and fatal crashes underrepresented, limiting the reliability of standard statistical analysis. To address these gaps, this study applies GeoShapley, an explainable machine learning (XAI) framework that captures both spatial and non-spatial effects. GeoShapley treats geographic location as an interactive predictor, allowing for interpretable, location-specific insights. We also apply the Synthetic Minority Oversampling Technique (SMOTE) to improve class balance and reduce model bias. The analysis uses crash data from 2,314 food delivery motorcycle incidents recorded in Taipei City in 2020. Results show that severe crashes are more likely on roads with higher speed limits, straight segments, intersections, and in suburban areas near restaurants. Male riders and signal violations are also strongly linked to higher crash severity. GeoShapley reveals that these risk factors vary significantly across locations, highlighting the importance of spatial heterogeneity in crash modeling. This study demonstrates the benefits of combining interpretable machine learning with spatial analysis and class-balancing techniques. The findings offer practical insights for developing targeted, location-specific safety interventions for food delivery motorcyclists in urban areas. Keywords: Food delivery motorcycles- Crash severity- GeoShapley- SMOTE- GeoAI Share Link | Plain Format | Corresponding Author (I Gede Brawiswa Putra)

322	Topic D: Geospatial Data Integration	ABS-228
Predicting Landslide Risk in Post-Fire Zones of Northern Thailand Oumkrue, S.(ab*), Rojanavasu, P.(a), Chaiwongsai, J.(a), Kantawong, K.(a), Rachata, N.(a), Deeprasertkul, P.(b) a)School of Information and Communication Technology, University of Phayao, Thailand b)Hydro Informatics Institute (Public Organization) (HII), Thailand Abstract Landslides are frequently found in Northern Thailand and especially in the rainy season that follows periods of forest fires. Wildfires, generally occurred from January to May, and they damaged the foliage on mountainous terrains. The wildfire damage weakens the soil cohesion and increases the risk of landslides during heavy or prolonged rainfall. This study aims to develop a spatial prediction model for post-wildfire landslide risk using machine learning techniques. It integrates three years of geosptial data which are daily rainfall from real-time telemetry station satellite-derived thermal hotspots historical landslide occurrences slope gradients from DEMs and land-use land-cover data. First in data preparation we use cumulative rainfall over 1-day 3-day and 7-day periods, hotspot counts within 1-year and 2-year windows, slope steepness, and land-use types. Next, we classify landslide risk into 3 levels as follows low, medium, and high, which are defined by analyzing rainfall intensity, hotspot intensity, and the density of historical landslides. The proposed system uses classification methods and a GIS map tool to generate maps that show landslide risk levels. The model performs with high classification accuracy and can identify multiple landslide risk zones. Furthermore, the analysis results suggest that areas previously affected by wildfires tend to show higher landslide exposure under equivalent rainfall conditions compared to areas without wildfire history. The result also shows rainfall accumulation thresholds that are associated with increased landslide risk in post-fire environments. Lastly we found that integrating rainfall, hotspot density, slope gradient and land use can raise the accuracy of predicting landslide risk zones and help create a landslide risk map. In conclusion, this study presents an integration of wildfire history and multi-temporal rainfall data with geospatial features and uses machine learning to enhance landslide risk assessment. Keywords: Landslide- Wildfire- Rainfall- Machine Learning Share Link | Plain Format | Corresponding Author (Sakorn Oumkrue)

323	Topic D: Geospatial Data Integration	ABS-236
Spatial Modeling of Transition Dynamics in Indonesia^s New Capital (IKN): Planning, Monitoring, and Policy Implications Amhar F. (1), Wibowo A. (2), Sa^dianoor (3) 1) Research Professor, Research Center for Geoinformatics, BRIN, Indonesia 2) Lecturer, Faculty for Forestry, Univ. Palangkaraya, Indonesia 3) Public Works and Housing Agency, Central Hulu Sungai Regency, Indonesia Abstract The relocation of Indonesia^s national capital from Jakarta to Nusantara (IKN) represents one of the most ambitious spatial, political, and environmental transitions in Southeast Asia in recent decades. While massive infrastructure development is underway, questions remain regarding the sustainability, ecological integrity, and spatial efficiency of the transition. This study aims to assess the spatial dynamics of IKN^s development using multi-temporal remote sensing data, integrated with land use modeling and institutional transition frameworks. We employ Sentinel-2 and Landsat 8/9 imagery (2018-2025) to analyze land cover changes, deforestation trends, and urban sprawl patterns across the IKN and surrounding buffer zones. These geospatial observations are combined with a CA-Markov urban growth simulation to evaluate various policy-driven scenarios under different population and governance assumptions. Furthermore, we introduce a spatially-explicit ^Transition Maturity Index (TMI)^ to measure the functional readiness of each urban block, including infrastructure completeness, ecological resilience, and service accessibility. Preliminary findings indicate rapid landscape conversion in certain sectors, yet uneven institutional and population migration trends. The model also reveals potential inefficiencies in infrastructure placement that could result in over-investment in low-utility zones. Our results suggest that spatial intelligence must play a greater role in guiding phased development, enabling a more adaptive, cost-effective, and environmentally responsive transition toward national capital relocation. This paper provides both methodological and policy-relevant insights for planners, spatial analysts, and decision-makers navigating the complexity of large-scale capital transitions in developing nations. Keywords: capital relocation, spatial policy, remote sensing, urban growth modeling, transition maturity Index (TMI) Share Link | Plain Format | Corresponding Author (Fahmi Amhar)

324	Topic D: Geospatial Data Integration	ABS-245
Delineation of Urban Center Area Based on Remote Sensing Data 1Irland Fardani, 2Albertus Deliar, 2*Anjar Dimara Sakti 1Doctoral Program in Geodesy and Geomatics Engineering, Faculty of Earth Science and Technology, Institut Teknologi Bandung, Bandung, 40132, Indonesia 2Center for Spatial Data Infrastructure, Bandung Institute of Technology, Bandung 40132, Indonesia Abstract The delineation of urban areas in Indonesia is determined by the government. However, in practice, the boundaries of these city areas do not conform to the existing delineation. The influence of population and economic activity variables sometimes causes urban areas to not conform to the predetermined delineation of city areas, better known as the urban sprawl effect. The purpose of this study is the urban sprawl effect of Makassar City. The methodology used in this study is the Degree of Urbanization (DoU) using a remote sensing data approach. This method uses two main data sets: population data and building data. The results of this study identified the urban center area of Makasar City, which is an expansion due to the urban sprawl effect. It was found that the influence of Makassar City has expanded far beyond the existing urban area delineation. Keywords: Urban center, remote sensing, urban sprawl, population. Share Link | Plain Format | Corresponding Author (Irland Fardani)

325	Topic E: Sustainable Development Goals	ABS-3
Advancing Malaysia^s Forest Monitoring Through Remote Sensing: Integrating Landsat and Google Earth Engine for Carbon Stock Assessment Hamdan O.1, Muhamad Afizzul M.1, Simon D.2 & Karen C.2 1. Forest Research Institute Malaysia (FRIM), 52109 Kepong, Selangor 2. Ministry of Natural Resources & Environmental Sustainability (NRES), 62000 Federal Territory Putrajaya Abstract Malaysia remains firmly committed to maintaining at least 50% forest cover, underscoring its role in global climate mitigation through advanced spatial data integration. To enhance the accuracy and transparency of forest monitoring, Malaysia adopts a stepwise reporting approach aligned with Biennial Update Reports (BUR-3 in 2018 and BUR-4 in 2022) and the Biennial Transparency Report (BTR-1 in 2024). This study leverages Google Earth Engine (GEE)-derived activity data and Landsat imagery collected at approximately five-year intervals from 2005 to 2024, facilitating a transition from gazetted area-based statistics to satellite-driven verification. This methodological refinement significantly improves the precision of Forest Reference Level (FRL) and Forest Reference Emission Level (FREL) assessments, strengthening national reporting frameworks. The analysis reveals that Malaysia^s forested areas span 18,497,327 hectares, covering approximately 54% of the nation^s landmass. A comprehensive forest carbon inventory estimates the annual carbon sink at 238,262 MgCO2e for 2024, while total forest carbon stock has exhibited a slight decline from 3.19 billion MgC in 2005 to 3.08 billion MgC in 2024. These findings highlight Malaysia^s substantial contribution to climate change mitigation, as its forests continue to sequester approximately 65% of the nation^s total emissions. By harnessing cutting-edge remote sensing technologies, Malaysia reinforces the integrity of its forest monitoring system, enabling data-driven policy decisions and reaffirming its commitment to sustainable forest management and long-term climate resilience. Keywords: forest monitoring, remote sensing, carbon stock assessment, GEE Share Link | Plain Format | Corresponding Author (HAMDAN OMAR)

326	Topic E: Sustainable Development Goals	ABS-17
Geostatistics of Soil Organic Carbon over Peatlands with Productive Oil Palm Crops Yusrizal, Budi Mulyanto, Heru Bagus Pulunggono and Bambang Hendro Trisasongko Department of Soil Science and Land Resource, Bogor Agricultural University, Jalan Meranti, Dramaga, Bogor 16680, Indonesia Study Program of Agrotechnology, Faculty of Agriculture, Universitas Teuku Umar, Meureubo, Aceh Barat 23568, Indonesia Study Program of Smart Agriculture, Faculty of Agriculture, Jalan Meranti, Dramaga, Bogor 16680, Indonesia Abstract Tropical peatlands are globally significant carbon sinks, characterized by their high organic matter content and waterlogged conditions that inhibit decomposition. However, widespread conversion of these ecosystems into oil palm plantations has disrupted peat hydrology and accelerated soil organic carbon (SOC) losses, thereby intensifying greenhouse gas emissions and ecological degradation. This study investigates spatial heterogeneity of SOC in Indonesian tropical peatlands cultivated with oil palm using geostatistical modeling. A total of 96 soil samples were collected during the dry season (February 2024), stratified by peat thickness (<3 m and >3 m), sampling depth (0-30 cm and 31-60 cm), and distance from secondary drainage canals (10, 25, 50, 75, 100, and 150 meters). The SOC content was determined using the Walkley-Black wet oxidation method and analyzed spatially using Kriging. SOC concentrations ranged from 29.55% to 60.85%, with elevated mean values in deeper sampling layers (31-60 cm) and areas with peat thickness exceeding 3 meters. Levels of SOC also increased progressively with greater distances from drainage canals, indicating reduced oxidative decomposition in hydrologically stable zones. Empirical semivariogram analysis confirmed strong spatial dependence, and ordinary kriging interpolation demonstrated sufficiency in predictive modeling. Integration of stratified sampling design, peat thickness categorization, and drainage proximity into the spatial model yielded a representative SOC distribution, despite some anomalies. These results highlight the efficacy of geostatistical techniques for capturing SOC variability in peatland ecosystems and provide critical insight for sustainable peatland management, restoration planning, and climate change mitigation Keywords: Tropical peatlands, Soil organic carbon, Oil palm plantations, Drainage, Kriging Share Link | Plain Format | Corresponding Author (yusrizal yusrizal)

327	Topic E: Sustainable Development Goals	ABS-18
ForensicSAR approach for detecting precursor deformation prior to the collapse of Derna Dam in Libya Arliandy Pratama (a,b*), Wataru Takeuchi (b) (a) Dept. of Civil Engineering, The University of Tokyo, Tokyo, Japan *arliandyarbad[at]g.ecc.u-tokyo.ac.jp (b) Institute of Industrial Science, The University of Tokyo, Tokyo, Japan Abstract On 10-11 September 2023, Storm Daniel triggered cascading failures of the Bu Mansour (Upper Derna) and Al-Bilad (Lower Derna) dams in Libya. We develop ForensicSAR, a workflow that densifies on-structure InSAR sampling and couples satellite-derived deformation metrics with lightweight finite-element (FEM) hypothesis testing. Dual-orbit Sentinel-1 time series (2019-2024) are decomposed into vertical (U) and east-west (E) components, and Tracy-Widom PS selection (TW-PSI) recovers ~40% more persistent scatterers over low-coherence dam bodies relative to ADI-PSI, stabilizing pre-event velocity, acceleration, and &#916-slope estimates. Within a 12-month pre-event window, we detect a localized precursor at Bu Mansour, concentrated over the crest and eastern sectors with elevated &#8739-&#916-slope| and zscores, whereas Al-Bilad shows weaker or inconsistent signals. Preliminary 2D Mohr-Coulomb FEM sections reproduce crest settlement and core-face stress concentrations consistent with central-core weakening under elevated head, providing a physically plausible interpretation of the remote-sensing evidence. Overall, integrating TW-PSI-densified InSAR with lightweight FEM discriminates plausible failure mechanisms and yields screening-level triggers for structural-health-monitoring prioritization in data-limited dam inventories, offering a scalable approach to dam-safety management. Keywords: ForensicSAR, SHM, InSAR, TW-PSI, Dam Share Link | Plain Format | Corresponding Author (Arliandy Pratama)

328	Topic E: Sustainable Development Goals	ABS-26
Please Just TryProgress Review and Future Directions for Remote Sensing in Planetary Health to Submit This Sample Abstract Mazlan Hashim Universiti Teknologi Malaysia Abstract The complex interrelationship between human well-being and the natural systems of Earth is highlighted by planetary health. Remote sensing technologies have become essential instruments for tracking ecological changes, promoting sustainable development, and guiding public health initiatives as environmental constraints increase. With an emphasis on planetary health concerns, this paper describes a new development in remote sensing applications and places it within the larger framework of international research. Sustainable land use, biodiversity preservation, climate action, and water resource management are important uses. Methodological advancements including cloud-based platforms, multisensor integration, and AI-driven analytics are also highlighted in the abstract. There is discussion of issues including policy integration, technical capability, and data accessibility. In accordance with the Sustainable Development Goals (SDGs), this paper ends with some future directions to improve planetary health monitoring using Earth observation. Keywords: Planetary Health, Remote Sensing, Sustainable Development Goals, Earth Observation, Environmental Monitoring Share Link | Plain Format | Corresponding Author (Mazlan Hashim)

329	Topic E: Sustainable Development Goals	ABS-27
Enhancing Flood Resilience: A GIS-Based Analysis of Evacuation Centre Site Suitability Cheah Wei Chee, Nurul Ashikin Bte Mabahwi Universiti Sains Malaysia Abstract Flooding is one of the most frequent and destructive natural hazards affecting Southeast Asia, particularly in low-lying urban areas such as Kuantan, Malaysia. The increasing intensity of flood events due to climate change and urban expansion underscores the urgent need for resilient infrastructure planning. This study aims to support disaster preparedness and enhance flood resilience by identifying optimal locations for evacuation centres using a GIS-based Multi-Criteria Evaluation (MCE) framework. The methodology integrates remote sensing data, spatial analysis, and the Analytical Hierarchy Process (AHP) to assess land suitability. Key criteria include elevation, slope, proximity to disaster-prone areas, landslides, floods, river, and land use. Each layer was standardized, weighted based on literature review, and combined using a weighted overlay analysis in ArcGIS. The final suitability map classifies land into five categories: Extremely Suitable, Very Suitable, More Suitable, Moderately Suitable, and Less Suitable. Results indicate that among the 127 evacuation centres evaluated, 31.50% were categorized as Less Suitable, 7.09% as Moderately Suitable, 19.69% as More Suitable, 26.77% as Very Suitable, and 14.96% as Extremely Suitable, and. These findings reveal a limited distribution of highly suitable areas, highlighting the importance of integrating geospatial hazard data with topographic constraints in planning decisions. This research demonstrates the practical application of remote sensing and GIS in improving urban flood resilience and emergency response planning. It contributes to Sustainable Development Goals by supporting inclusive, safe, and disaster-resilient infrastructure (SDG 11) and promoting climate adaptation strategies (SDG 13). The study offers a transferable and scalable approach that can benefit disaster-prone regions across Asia, aligning with the broader goals of advancing remote sensing science for sustainable development. Keywords: Flood resilience, evacuation, site suitability analysis, remote sensing, GIS, AHP Share Link | Plain Format | Corresponding Author (Wei Chee Cheah)

330	Topic E: Sustainable Development Goals	ABS-289
Geospatial Assessment of Cultural Heritage Vulnerability to Earthquake Hazards in West Sulawesi: A Contribution to Disaster Risk Reduction and SDG Resilience Yadi Mulyadi , Ilham Alimuddin , Muhammad Nur Akram Department of Archaeology, Hasanuddin University Department of Geology, Hasanuddin University National Archaeology Indonesia Abstract West Sulawesi faces heightened seismic risks that threaten not only human settlements but also cultural heritage assets critical to education, tourism, and identity preservation. This study presents a geospatial vulnerability assessment of 70 cultural heritage sites using an integrated modeling approach that combines Frequency Ratio (FR) and Fuzzy Logic (FL), validated through Receiver Operating Characteristic (ROC) analysis. The results categorize heritage sites into three vulnerability levels-high (48 sites), moderate (20 sites), and low (2 sites)-based on spatial factors such as lithology, settlement proximity and density, road access, earthquake magnitude, fault line proximity, and epicenter distance. The FR model achieved an AUC Success of 0.753 and AUC Predictive of 0.598, indicating robust predictive capability. By identifying spatial patterns of vulnerability, this research supports geospatial disaster management strategies and informs targeted mitigation planning. Moreover, it aligns with Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), by promoting resilient infrastructure and safeguarding cultural heritage in disaster-prone regions. The findings offer actionable insights for policymakers, conservationists, and urban planners seeking to integrate cultural resilience into broader disaster risk frameworks. Keywords: Cultural heritage vulnerability, earthquake hazard, geospatial modeling, SDG resilience, West Sulawesi, Frequency Ratio, Fuzzy Logic Share Link | Plain Format | Corresponding Author (Yadi Mulyadi)

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