{"total":90,"rows":[{"id":"2311.17179","title":"SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery","created":"2023-11-28","cited_by_count":151,"area":null},{"id":"2304.06798","title":"On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligence","created":"2023-04-13","cited_by_count":88,"area":null},{"id":"2402.01444","title":"Mission Critical -- Satellite Data is a Distinct Modality in Machine Learning","created":"2024-02-02","cited_by_count":77,"area":null},{"id":"2305.01118","title":"CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations","created":"2023-05-01","cited_by_count":69,"area":null},{"id":"2310.06743","title":"Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks","created":"2023-10-10","cited_by_count":61,"area":null},{"id":"2306.02564","title":"Spatial Implicit Neural Representations for Global-Scale Species Mapping","created":"2023-06-05","cited_by_count":59,"area":null},{"id":"2111.04006","title":"A Review of Location Encoding for GeoAI: Methods and Applications","created":"2021-11-07","cited_by_count":54,"area":null},{"id":"2306.17624","title":"Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions","created":"2023-06-30","cited_by_count":39,"area":null},{"id":"2406.15658","title":"TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning","created":"2024-06-21","cited_by_count":31,"area":null},{"id":"2105.09392","title":"Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions","created":"2021-05-19","cited_by_count":24,"area":null},{"id":"2402.01749","title":"Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Models","created":"2024-01-30","cited_by_count":23,"area":null},{"id":"2209.15458","title":"Towards General-Purpose Representation Learning of Polygonal Geometries","created":"2022-09-29","cited_by_count":22,"area":null},{"id":"2310.00583","title":"City Foundation Models for Learning General Purpose Representations from OpenStreetMap","created":"2023-10-01","cited_by_count":21,"area":null},{"id":"2111.10144","title":"Positional Encoder Graph Neural Networks for Geographic Data","created":"2021-11-19","cited_by_count":20,"area":null},{"id":"2505.14361","title":"Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives","created":"2025-05-20","cited_by_count":18,"area":"mllm"},{"id":"2203.03253","title":"Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information","created":"2022-03-07","cited_by_count":15,"area":null},{"id":"2309.07438","title":"Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges","created":"2023-09-14","cited_by_count":14,"area":null},{"id":"2109.15044","title":"SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss","created":"2021-09-30","cited_by_count":13,"area":null},{"id":"2408.14806","title":"Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications","created":"2024-08-27","cited_by_count":13,"area":null},{"id":"2411.04381","title":"TrajGPT: Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal Model","created":"2024-11-07","cited_by_count":13,"area":null},{"id":"2105.11207","title":"Mapping oil palm density at country scale: An active learning approach","created":"2021-05-24","cited_by_count":12,"area":null},{"id":"2304.07298","title":"Road Network Representation Learning: A Dual Graph based Approach","created":"2023-04-13","cited_by_count":12,"area":null},{"id":"2304.07750","title":"GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates","created":"2023-04-16","cited_by_count":12,"area":null},{"id":"2503.16683","title":"GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations","created":"2025-03-20","cited_by_count":10,"area":null},{"id":"2511.02101","title":"Measuring the Intrinsic Dimension of Earth Representations","created":"2025-11-03","cited_by_count":10,"area":null},{"id":"2310.19626","title":"Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities","created":"2023-10-30","cited_by_count":9,"area":null},{"id":"2305.01079","title":"Bird Distribution Modelling using Remote Sensing and Citizen Science data","created":"2023-05-01","cited_by_count":8,"area":null},{"id":"2206.02127","title":"DeeprETA: An ETA Post-processing System at Scale","created":"2022-06-05","cited_by_count":7,"area":null},{"id":"2508.19305","title":"Geo2Vec: Shape- and Distance-Aware Neural Representation of Geospatial Entities","created":"2025-08-26","cited_by_count":7,"area":null},{"id":"2408.17258","title":"Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach","created":"2024-08-30","cited_by_count":6,"area":null},{"id":"2503.18142","title":"LocDiff: Identifying Locations on Earth by Diffusing in the Hilbert Space","created":"2025-03-23","cited_by_count":6,"area":"diffusion"},{"id":"2506.02744","title":"Enriching Location Representation with Detailed Semantic Information","created":"2025-06-03","cited_by_count":6,"area":null},{"id":"2508.11739","title":"Scalable Geospatial Data Generation Using AlphaEarth Foundations Model","created":"2025-08-15","cited_by_count":5,"area":null},{"id":"2605.26036","title":"CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities","created":"2026-05-25","cited_by_count":5,"area":null},{"id":"2410.19256","title":"Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Prediction","created":"2024-10-25","cited_by_count":4,"area":null},{"id":"2504.16942","title":"S2Vec: Self-Supervised Geospatial Embeddings for the Built Environment","created":"2025-04-10","cited_by_count":4,"area":null},{"id":"2506.01297","title":"MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale","created":"2025-06-02","cited_by_count":4,"area":null},{"id":"2506.14070","title":"Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places","created":"2025-06-17","cited_by_count":4,"area":null},{"id":"2405.18459","title":"Probing the Information Theoretical Roots of Spatial Dependence Measures","created":"2024-05-28","cited_by_count":3,"area":null},{"id":"2407.00748","title":"Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction","created":"2024-06-30","cited_by_count":3,"area":null},{"id":"2507.03062","title":"BERT4Traj: Transformer Based Trajectory Reconstruction for Sparse Mobility Data","created":"2025-07-03","cited_by_count":3,"area":null},{"id":"2509.23482","title":"GeoBS: Information-Theoretic Quantification of Geographic Bias in AI Models","created":"2025-09-27","cited_by_count":3,"area":null},{"id":"2601.21149","title":"Mobility-Embedded POIs: Learning What A Place Is and How It Is Used from Human Movement","created":"2026-01-29","cited_by_count":3,"area":null},{"id":"2602.00392","title":"Localized, High-resolution Geographic Representations with Slepian Functions","created":"2026-01-30","cited_by_count":3,"area":null},{"id":"2201.10489","title":"Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions","created":"2022-01-25","cited_by_count":2,"area":null},{"id":"2202.04755","title":"DeepSSN: a deep convolutional neural network to assess spatial scene similarity","created":"2022-02-07","cited_by_count":2,"area":null},{"id":"2310.00413","title":"SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution","created":"2023-09-30","cited_by_count":2,"area":null},{"id":"2312.04055","title":"Jointly spatial-temporal representation learning for individual trajectories","created":"2023-12-07","cited_by_count":2,"area":null},{"id":"2402.15398","title":"TransFlower: An Explainable Transformer-Based Model with Flow-to-Flow Attention for Commuting Flow Prediction","created":"2024-02-23","cited_by_count":2,"area":null},{"id":"2502.15032","title":"GeoAggregator: An Efficient Transformer Model for Geo-Spatial Tabular Data","created":"2025-02-20","cited_by_count":2,"area":null},{"id":"2510.09894","title":"Beyond AlphaEarth: Toward Human-Centered Geospatial Foundation Models via POI-Guided Contrastive Learning","created":"2025-10-10","cited_by_count":2,"area":null},{"id":"2606.12146","title":"nD-RoPE: A Generalized RoPE for n-Dimensional Position Embedding","created":"2026-06-10","cited_by_count":2,"area":null},{"id":"2606.27514","title":"Tessellating The Earth","created":"2026-06-25","cited_by_count":2,"area":null},{"id":"2109.07583","title":"Network representation learning systematic review: ancestors and current development state","created":"2021-09-14","cited_by_count":1,"area":null},{"id":"2310.17643","title":"Where you go is who you are -- A study on machine learning based semantic privacy attacks","created":"2023-10-26","cited_by_count":1,"area":null},{"id":"2408.14762","title":"Explainable Hierarchical Urban Representation Learning for Commuting Flow Prediction","created":"2024-08-27","cited_by_count":1,"area":null},{"id":"2409.18865","title":"Positional Encoder Graph Quantile Neural Networks for Geographic Data","created":"2024-09-27","cited_by_count":1,"area":null},{"id":"2410.10937","title":"Hybrid Spatial Representations for Species Distribution Modeling","created":"2024-10-14","cited_by_count":1,"area":null},{"id":"2503.02036","title":"Latent Domain Modeling Improves Robustness to Geographic Shifts","created":"2025-03-03","cited_by_count":1,"area":null},{"id":"2505.18477","title":"ZooplanktonBench: A Geo-Aware Zooplankton Recognition and Classification Dataset from Marine Observations","created":"2025-05-24","cited_by_count":1,"area":null},{"id":"2511.20610","title":"Building a Foundation Model for Trajectory from Scratch","created":"2025-11-25","cited_by_count":1,"area":null},{"id":"2601.23064","title":"HierLoc: Hyperbolic Entity Embeddings for Hierarchical Visual Geolocation","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2604.07522","title":"Training-free Spatially Grounded Geometric Shape Encoding (Technical Report)","created":"2026-04-08","cited_by_count":1,"area":null},{"id":"2605.20134","title":"TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning","created":"2026-05-19","cited_by_count":1,"area":null},{"id":"2606.24997","title":"What's in an Earth Embedding? An Explainability Analysis of Location Encoders","created":"2026-06-23","cited_by_count":1,"area":null},{"id":"2103.00483","title":"Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs","created":"2021-02-23","cited_by_count":0,"area":null},{"id":"2112.12219","title":"SAMCNet for Spatial-configuration-based Classification: A Summary of Results","created":"2021-12-22","cited_by_count":0,"area":null},{"id":"2401.10144","title":"Exploiting Hierarchical Interactions for Protein Surface Learning","created":"2024-01-17","cited_by_count":0,"area":null},{"id":"2406.07049","title":"GridPE: A Grid Cell-Inspired Unified Position Embedding for Arbitrary-Dimensional Spaces","created":"2024-06-11","cited_by_count":0,"area":null},{"id":"2503.08290","title":"SegDesicNet: Lightweight Semantic Segmentation in Remote Sensing with Geo-Coordinate Embeddings for Domain Adaptation","created":"2025-03-11","cited_by_count":0,"area":null},{"id":"2504.08620","title":"Efficient Mixture of Geographical Species for On Device Wildlife Monitoring","created":"2025-04-11","cited_by_count":0,"area":null},{"id":"2506.05016","title":"Multi-Point Proximity Encoding For Vector-Mode Geospatial Machine Learning","created":"2025-06-05","cited_by_count":0,"area":null},{"id":"2506.17302","title":"Fine-Scale Soil Mapping in Alaska with Multimodal Machine Learning","created":"2025-06-17","cited_by_count":0,"area":null},{"id":"2507.09137","title":"POIFormer: A Transformer-Based Framework for Accurate and Scalable Point-of-Interest Attribution","created":"2025-07-12","cited_by_count":0,"area":null},{"id":"2508.01240","title":"RelMap: Reliable Spatiotemporal Sensor Data Visualization via Imputative Spatial Interpolation","created":"2025-08-02","cited_by_count":0,"area":null},{"id":"2508.06584","title":"Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution","created":"2025-08-08","cited_by_count":0,"area":null},{"id":"2510.06473","title":"Deep Generative Model for Human Mobility Behavior","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2511.17954","title":"A multi-view contrastive learning framework for spatial embeddings in risk modelling","created":"2025-11-22","cited_by_count":0,"area":null},{"id":"2601.08750","title":"A Geolocation-Aware Multimodal Approach for Ecological Prediction","created":"2026-01-13","cited_by_count":0,"area":null},{"id":"2605.06906","title":"TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond","created":"2026-05-07","cited_by_count":0,"area":"lr"},{"id":"2605.06990","title":"TrajGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.12276","title":"NARA: Anchor-Conditioned Representation Learning for Heterogeneous Vector Geoentities","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2606.08046","title":"OSMGraphCLIP: Learning Global Location Representations from OpenStreetMap Graphs","created":"2026-06-06","cited_by_count":0,"area":null},{"id":"2606.22546","title":"Venice-H1: Failure-Aware Query Re-Ranking with Multi-Scale Grid Signatures for Referring Image Segmentation","created":"2026-06-21","cited_by_count":0,"area":null},{"id":"2606.23453","title":"Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2607.08281","title":"Enhancing the KidSat Model: Integrating Geographical Encoding and Data Quality Assessment for Childhood Poverty Prediction","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2609.21872","title":"Chronosphere: Space-Time Tessellation of Local Climate Experts","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2609.22117","title":"LE4Mob: Towards Inductive, Distance-Aware and General-Purpose Location Embedding for Human Mobility Modelling","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2609.25454","title":"MIND the Gap: A Geographic Implicit Neural Representation with Adjustable Spatial Scale","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2610.17190","title":"CartoVec: General-Purpose Location Embeddings from Open Map Data","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}