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Spatiotemporal deformation analysis of glaciers and surrounding landscapes in the Shishapangma region using InSAR techniques

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Journal of Mountain science 2025年第1期22卷 210-229页

作者： LIANG Wenli LIU Rui JI Qin KOU Pinglang Chongqing Key Laboratory of GIS Application School of Geography and TourismChongqing Normal UniversityChongqing 401331China National Earth System Science Data Center Beijing 100000China Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism(TMDPDMCT)Chongqing University of Posts and TelecommunicationsChongqing 400065China

Glacier dynamics in the Himalayan midlatitudes,particularly in regions like the Shishapangma,are not yet fully understood,especially the localized topographic and climatic impacts on glacier *** study analyzes the spatiotemporal characteristics of glacier surface deformation in the Shishapangma region using the Small Baseline Subset(SBAS)Interferometric Synthetic Aperture Radar(In SAR)*** analysis reveals an average deformation rate of-4.02±17.65 mm/yr across the entire study area,with glacier regions exhibiting significantly higher rates of uplift(16.87±13.20 mm/yr)and subsidence(20.11±14.55 mm/yr)compared to non-glacier *** identifies significant surface lowering on the mountain flanks and localized uplift in certain catchments,emphasizing the higher deformation rates in glacial areas compared to non-glacial *** found a strong positive correlation between temperature and cumulative deformation(correlation coefficient of 0.63),particularly in glacier areas(0.82).The research highlights the role of temperature as the primary driver of glacier wastage,particularly at lower elevations,with strong correlations found between temperature and cumulative *** also indicates the complex interactions between topographic features,notably,slope gradient,which shows a positive correlation with subsidence rates,especially for slopes below 35°.South-,southwest-,and west-facing slopes exhibit significant uplift,while north-,northeast-,and east-facing slopes predominantly ***,we identified transition zones between debris-covered glaciers and clean ice as areas of most intense deformation,with average rates exceeding 30 mm/yr,highlighting these as potential high-risk zones for *** study comprehensively analyzes the deformation characteristics in both glacier and non-glacier areas in the Shishapangma region,revealing the complex interplay of topographic,climatic,and hydrological factors influencing glacier dynamic

关键词： Glacier melting SBAS-InSAR Topographic factors Temperature change Middle Himalayas

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HOW DO THE PROFESSIONAL PLAYERS SELECT THEIR SHOT LOCATIONS? AN ANALYSIS OF FIELD GOAL ATTEMPTS VIA BAYESIAN ADDITIVE REGRESSION TREES

arXiv

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arXiv 2025年

作者： Cao, Jiahao Yang, Hou-Cheng Hu, Guanyu Center for Spatial Temporal Modeling for Applications in Population Sciences Department of Biostatistics and Data Science The University of Texas Health Science Center HoustonTX United States Department of Statistics Florida State University Florida United States

Basketball analytics has significantly advanced our understanding of the game, with shot selection emerging as a critical factor in both individual and team performance. With the advent of player tracking technologies, a wealth of granular data on shot attempts has become available, enabling a deeper analysis of shooting behavior. However, modeling shot selection presents unique challenges due to the spatial and contextual complexities influencing shooting decisions. This paper introduces a novel approach to the analysis of basketball shot data, focusing on the spatial distribution of shot attempts, also known as intensity surfaces. We model these intensity surfaces using a Functional Bayesian Additive Regression Trees (FBART) framework, which allows for flexible, nonparametric regression, and uncertainty quantification while addressing the nonlinearity and nonstationarity inherent in shot selection patterns to provide a more accurate representation of the factors driving player performance;we further propose the Adaptive Functional Bayesian Additive Regression Trees (AFBART) model, which builds on FBART by introducing adaptive basis functions for improved computational efficiency and model fit. AFBART is particularly well suited for the analysis of two-dimensional shot intensity surfaces and provides a robust tool for uncovering latent patterns in shooting behavior. Through simulation studies and real-world applications to NBA player data, we demonstrate the effectiveness of the model in quantifying shooting tendencies, improving performance predictions, and informing strategic decisions for coaches, players, and team managers. This work represents a significant step forward in the statistical modeling of basketball shot selection and its applications in optimizing game strategies. Copyright © 2025, The Authors. All rights reserved.

关键词： Basketball

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A review on global spatial distribution,sources and toxicity of perfluoroalkyl acid and prospect in the cryosphere

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Journal of Mountain science 2025年第1期22卷 167-183页

作者： LI Bowen Du Wentao KANG Shichang CHEN Jizu JIANG Youyan YIN Lichen LU Changsheng LIU Hongyi State Key Laboratory of Cryospheric Science and frozen soil engineering Northwest Institute of Eco-Environment and ResourcesChinese Academy of Sciences(CAS)Lanzhou 730000China University of Chinese Academy of Sciences Beijing 100049China Gansu Provincial Industry Technology Center of Intelligent Equipment and Big Data for Disaster Prevention Northwest Institute of Eco-Environment and ResourcesChinese Academy of Sciences(CAS)Lanzhou 730000China Lanzhou Regional Climate Center Lanzhou 730000China School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Energy and Power Engineering Lanzhou University of TechnologyLanzhou 730000China Zhangye Meteorological Service Zhangye 734000China

Perfluoroalkyl acid analogs(PFAAs)are a class of chemically stable environmentally persistent organic pollutants(POPs)that are difficult to degrade and have a strong capacity to accumulate in the human *** have been found to be biotoxic to humans and have been detected in various environmental media,especially in the cryosphere at trace *** cryosphere,sensitively responds to climate change,plays a crucial role in the global water,carbon and energy ***,researches on cryosphere PFAAs especially in Tibetan Plateau(TP)is ***,we summarize the physicochemical properties,physiological toxicity,spatiotemporal distribution,sources,diffusion and migration pathways,as well as analysis and removal methods of PFAAs in the cryosphere *** results show that PFAAs pollutants are mainly produced and distributed in the more economically developed countries in Europe and the United States,as well as in East Asia,and PFAAs can be transported by atmospheric circulation and water cycle to remote regions including cryosphere *** current detection methods for PFAAs in cryosphere need to be further refined for increased accuracy and *** is also a need to develop more effective removal methods that will reduce the environmental and human threats posed by these ***,we propose key scientific questions for future research in cryosphere including PFAAs redistribution influenced by cryosphere changes,human activities,and the interaction of other spheres.

关键词： Perfluoroalkyl acid Cryosphere Physiological toxicity Analytical method Removal method

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Analysis Geomorphic Flood Index in Determining Flood-Prone Areas in East Kalimantan Province, Indonesia

Analysis Geomorphic Flood Index in Determining Flood-Prone A...

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2024 Annual Conference, ASPRS 2024

作者： Virtriana, Riantini Amalia, Annisa Fitri Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Harto, Agung Budi Deliar, Albertus Geospatial Information Science and Technology Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung40132 Indonesia Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung40132 Indonesia Universitas Pendidikan Indonesia Bandung40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung40132 Indonesia

In 2022, there are 1,524 flood events out of a total of 3,531 disasters occurring in Indonesia. East Kalimantan Province ranked 20th among the 35 provinces in Indonesia with 28 flood occurrences in 2022, resulting in two fatalities. This highlights the urgent need for addressing flood hazard to prevent future casualties. There are some methods that can be used to determine the flood hazard in a region. In this research aims to identify flood-prone areas in East Kalimantan Province and evaluate the effectiveness of different mapping methods. The methodology that compares are the Pairwise Comparison method and Geomorphic Flood Index (GFI). The study's results provide flood-prone area maps of East Kalimantan Province using both methods, recommend the more efficient method for flood-prone area identification, and offer mitigation strategies based on the findings. This research is intended to serve as a reference for selecting the best method for identifying flood-prone areas and for informing policy decisions to mitigate flood impacts in East Kalimantan Province. © Author(s) 2025.

关键词： Hazards

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Spatio-temporal Dynamics and Drivers of Ecological Quality in Yulin City Using the MRSEI Model

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Journal of Resources and Ecology 2025年第2期16卷 340-355页

作者： MU Weichen HE Zhilin CHEN Yanglong GAO Dongkai YUE Tianming QIN Fen College of Geographical Sciences Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou450046China Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Ministry of EducationHenan UniversityKaifengHenan 475004China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan UniversityKaifengHenan 475004China Henan Technology Innovation Center of Spatial-Temporal Big Data Henan UniversityZhengzhou 450046China.

Urbanization has resulted in growing ecological pressures on cities,necessitating assessments of urban ecological ***-term characterization of regional dynamics and drivers is critical for environmental *** study proposes an enhanced ecological quality model(MRSEI)incorporating vegetation cover and EVI rather than just *** MRSEI model was applied to analyse ecological quality in Yulin City during 2000-2018 using Landsat TM/OLI data on Google Earth *** detectors also quantified anthropo-genic and environmental influences on the study *** results are summarized as follows:(1)MRSEI showed an average correlation coefficient of 0.840 with other indices,demonstrating higher representativeness than indi-vidual *** principal component analysis indicated a 12.88%increase in explained *** also exhibited significantly improved identification of roads,villages,and unused lands over RSEI,better matching ground conditions,and suitability for regional ecological assessment.(2)During 2000-2020,the average MRSEI in Yulin City was 0.481,peaking at 0.518 in 2018,indicating general ecological improvement over ***,conditions were better in the southeast than *** 38.81%of the area showed significant improvement,10.15%exhibited significant deterioration,concentrated in western Dingbian and Jingbian counties,highlighting areas requiring enhanced protection.(3)Ecological conditions in Yulin City remained stable over ***-high clusters were concentrated in eastern counties(Qingjian,Wubao,Jia,Fugu)and central lower-altitude areas near Yokoyama and ***-low clusters predominated in the northern Yuyang desert and high-altitude western Dingbian regions.(4)Enhanced vegetation cover had the greatest influence in improving Yulin’s ecological *** was the most impactful environmental driver,while precipitation and land use change interactions showed the strongest combined *** cont

关键词： Google Earth Engine improved remote sensing-based ecological indicator temporal and spatial variation geographical detector

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ATCM: Aerial-Terrestrial LiDAR-based Collaborative Simultaneous Localization and Mapping

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Xu, Yuhang Chen, Chi Yang, Bisheng Wu, Weitong Sun, Shangzhe Wang, Zhiye Li, Liuchun Zou, Qin Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Engineering Research Center for SpatioTemporal Data Smart Acquisition and Application Ministry of Education of China Institute of Geo-Spatial Intelligence Wuhan430079 China Hohai University School of Earth Sciences and Engineering Nanjing211100 China Wuhan University School of Computer Science Wuhan430079 China

Multi-robot collaborative simultaneous localization and mapping (C-SLAM) offers precise scene reconstruction over single-robot SLAM and enables the data fusion from heterogeneous robots. However, heterogeneous C-SLAM faces challenges in both accurate inter-robot loop closure detection and globally consistent data fusion due to inherent viewpoint disparities and heterogeneous data characteristics. This paper introduces ATCM, an Aerial-Terrestrial LiDAR-based C-SLAM method designed for heterogeneous robots without priori initial relative position. ATCM comprises three modules: single-robot front-end employing diverse SLAM methods, multi-robot loop closure detection, and global pose graph optimization. A novel LiDAR-based cross-view global loop descriptor is proposed for scan-to-scan heterogeneous inter-robot loop closure detection. By uniformly mapping cross-view information into the height domain and integrating dynamic height, the loop descriptor automatically achieves viewpoint correction. Additionally, we introduce a bidirectional loop detection algorithm that validates inter-robot loop closures through both forward and reverse detections. Finally, the two-stage global pose graph optimization integrates multi-source measurements, ensuring globally consistent mapping and localization with cross-view data. We have validated the effectiveness of ATCM on campus scenario datasets and the KITTI dataset, achieving a remarkable 21.95% improvement in trajectory accuracy and a 17.00% enhancement in map precision compared to high-precision point cloud maps, surpassing state-of-the-art LiDAR-based odometry methods. In the ablation experiments, the proposed loop descriptor achieved 97% accuracy in recognizing heterogeneous inter-robot loop closures. Moreover, compared to the traditional unidirectional method, the bidirectional loop detection method demonstrates up to a 31.2% improvement in loop closure accuracy. © 2025 IEEE.

关键词： SLAM robotics

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Development of location suitability prediction for health facilities using random forest machine learning in 2030 integrating remote sensing and GIS in West Java, Indonesia

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Environmental Advances 2025年 19卷

作者： Virtriana, Riantini Ihsan, Kalingga Titon Nur Anggraini, Tania Septi Deliar, Albertus Harto, Agung Budi Riqqi, Akhmad Sakti, Anjar Dimara Remote Sensing and GIS Research Group Faculty of Earth Science and Technology Bandung Institute of Technology Bandung 40132 Indonesia Center for Remote Sensing Bandung Institute of Technology Bandung 40132 Indonesia Doctor Program in Program Study of Geodesy and Geomatic Engineering Faculty of Earth Sciences and Technology Bandung 40132 Indonesia Geographic Information Science Universitas Pendidikan Indonesia Bandung 40111 Indonesia Center for Spatial Data Infrastructure Bandung Institute of Technology Bandung 40132 Indonesia

Access to healthcare facilities is crucial in the present day. Healthcare facilities must be proportional to the population in a given area. Therefore, it is important to increase the number of healthcare facilities in regions where there is an imbalance. Proper planning and a sustainability review are necessary when determining locations for healthcare facilities. Environmental changes can affect the suitability of a location in the future. Thus, planning that can predict future suitability conditions is required to ensure that the built locations have high sustainability. This study predicts the suitability of healthcare facility locations in 2030 in West Java using remote sensing and Geographic Information System (GIS). Both static and dynamic data processed at 30×30 meter intervals across West Java will be used. Geospatial and remote sensing data are utilized in the study. Dynamic parameter extrapolation uses data from 2000 to 2018. The random forest machine learning method is employed to obtain the suitability values for healthcare facility locations using existing health facility training data in West Java. The results show changes in the suitability classes of healthcare facilities in each region from 2018 to 2030, with some areas experiencing an increase or decrease in class. This research highlights consistently suitable locations, ensuring their sustainability as healthcare facility sites. © 2024

关键词： GIS Health Facility Machine Learning Remote Sensing

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RegionGCN: spatial-Heterogeneity-Aware Graph Convolutional Networks

arXiv

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arXiv 2025年

作者： Guo, Hao Wang, Han Zhu, Di Wu, Lun Fotheringham, A. Stewart Liu, Yu Institute of Remote Sensing and Geographical Information Systems School of Earth and Space Sciences Peking University Beijing China Lab Division of Landscape Architecture Faculty of Architecture The University of Hong Kong Pokfulam Hong Kong Lab Department of Geography Environment and Society University of Minnesota Twin Cities MinneapolisMN United States Spatial Data Science Center Department of Geography Florida State University TallahasseeFL United States

Modeling spatial heterogeneity in the data generation process is essential for understanding and predicting geographical phenomena. Despite their prevalence in geospatial tasks, neural network models usually assume spatial stationarity, which could limit their performance in the presence of spatial process heterogeneity. By allowing model parameters to vary over space, several approaches have been proposed to incorporate spatial heterogeneity into neural networks. However, current geographically weighting approaches are ineffective on graph neural networks, yielding no significant improvement in prediction accuracy. We assume the crux lies in the over-fitting risk brought by a large number of local parameters. Accordingly, we propose to model spatial process heterogeneity at the regional level rather than at the individual level, which largely reduces the number of spatially varying parameters. We further develop a heuristic optimization procedure to learn the region partition adaptively in the process of model training. Our proposed spatial-heterogeneity-aware graph convolutional network, named RegionGCN, is applied to the spatial prediction of county-level vote share in the 2016 US presidential election based on socioeconomic attributes. Results show that RegionGCN achieves significant improvement over the basic and geographically weighted GCNs. We also offer an exploratory analysis tool for the spatial variation of non-linear relationships through ensemble learning of regional partitions from RegionGCN. Our work contributes to the practice of Geospatial Artificial Intelligence (GeoAI) in tackling spatial heterogeneity. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Large-scale slum mapping in sub-Saharan Africa's major cities: Remote sensing and deep learning reveal strong slum growth in the urban periphery between 2016 and 2022

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Habitat International 2025年 161卷

作者： Büttner, Nicolas Stalder, Steven Volpi, Michele Suel, Esra Harttgen, Kenneth Department of Humanities Social and Political Sciences ETH Zurich Clausiusstrasse 37 Zurich 8092 Switzerland Swiss Data Science Center ETH Zurich and EPF Lausanne Andreasstrasse 5 Zurich 8092 Switzerland Centre for Advanced Spatial Analysis University College London 90 Tottenham Ct Rd London W1T 4TJ United Kingdom

Around half of sub-Saharan Africa's urban population lives in slums, yet data on the spatiotemporal development of slums remains scarce, impeding policies to alleviate urban poverty and inequality. We propose a solution to this problem by applying deep learning to open-access satellite imagery to map slums in 529 major cities across sub-Saharan Africa and track their spatiotemporal development. Our model produced 10m resolution ‘slum probability maps’ allowing timely and cost-effective tracking of slum growth. On this basis, we estimated that in 2022 the share of the urban population living in slums exceeded 50% in 274 cities, and in 84% of cities this share increased between 2016 and 2022, most severely in Middle and West Africa. Slum growth occurred primarily in the urban periphery, which tends to be missed in survey-based slum monitoring. © 2025 The Authors

关键词： Deep learning Informal settlements Satellite imagery Slums Sub-Saharan Africa

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Interoperability for Global Observation data by Ontological Information

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Tsinghua science and Technology 2008年第S1期13卷 336-342页

作者： Masahiko Nagai Masafumi Ono Ryosuke Shibasaki Earth Observation Data Integration and Fusion Research Initiative (EDITORIA) The University of Tokyo Center for Spatial Information Science The University of Tokyo

The Ontology registry system is developed to collect, manage, and compare ontological information for integrating global observation data. data sharing and data service such as support of metadata deign, structuring of data contents, support of text mining are applied for better use of data as data interoperability. Semantic network dictionary and gazetteers are constructed as a trans-disciplinary dictionary. Ontological information is added to the system by digitalizing text based dictionaries, developing 'knowledge writing tool' for experts, and extracting semantic relations from authoritative documents with natural language processing technique. The system is developed to collect lexicographic ontology and geographic ontology.

关键词： ontology interoperability data integration gazetteer semantic network dictionary

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