NARA: Anchor-Conditioned Relation-Aware Contextualization of Heterogeneous Geoentities

• URL: http://arxiv.org/abs/2605.12276v1
• Date: Tue, 12 May 2026 15:37:10 GMT
• Title: NARA: Anchor-Conditioned Relation-Aware Contextualization of Heterogeneous Geoentities
• Abstract summary: We propose NARA (Neural Anchor-conditioned Relation-Aware representation learning), a self-supervised framework for vector geoentities.<n> NARA learns context-dependent representations by jointly modeling semantics, geometry, and spatial relations within a unified framework.
• Score: 10.545074153193701
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Geospatial foundation models have primarily focused on raster data such as satellite imagery, where self-supervised learning has been widely studied. Vector geospatial data instead represent the world as discrete geoentities with explicit geometry, semantics, and structured spatial relations, including metric proximity and topological relationships. These relations jointly determine how entities interact within space, yet existing representation learning methods remain fragmented, often restricted to specific geometry types or partial spatial relations, limiting their ability to capture unified spatial context across heterogeneous geoentities. We propose NARA (Neural Anchor-conditioned Relation-Aware representation learning), a self-supervised framework for vector geoentities. NARA learns context-dependent representations by jointly modeling semantics, geometry, and spatial relations within a unified framework and captures relational spatial structure beyond proximity alone, enabling rich contextualized representations across heterogeneous geoentities of points, polylines, and polygons. Evaluation on building function classification, traffic speed prediction, and next point-of-interest recommendation shows consistent improvements over prior methods, highlighting the benefit of unified relational modeling for vector geospatial data.

Related papers

• Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models [12.200359585933766]
  Recent advances in self-supervised learning have given rise to Earth Observation Foundation Models (EOFMs)<n>This paper calls for a paradigm shift toward joint Spatial Representation Learning (SRL) in an unified embedding space that integrate perception with vector-based reasoning.<n>We contend that such integration is essential for developing next-generation geospatial AI systems capable of more accurate, interpretable, and grounded understanding of the Earth.
  arXiv  Detail & Related papers  (2026-06-01T15:21:58Z)
• GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval [54.741349848771144]
  GeoMamba is a geometry-driven framework for optical-SAR fine-grained retrieval.<n>GFI module enhances cross-modal feature interaction and incorporates structural priors.<n>GeoMamba outperforms existing methods, achieving 63.3% mAP and 77.0% Rank-1 accuracy in all-to-all retrieval setting.
  arXiv  Detail & Related papers  (2026-05-19T12:08:09Z)
• GeoMeld: Toward Semantically Grounded Foundation Models for Remote Sensing [33.72621953693859]
  GeoMeld is a large-scale multimodal dataset with approximately 2.5 million spatially aligned samples.<n>GeoMeld provides semantically grounded language supervision through an agentic captioning framework.<n>GeoMeld-FM is a pretraining framework that combines multi-pretext masked autoencoding over aligned modalities, JEPA representation learning, and caption-vision contrastive alignment.
  arXiv  Detail & Related papers  (2026-04-12T11:47:11Z)
• OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents [68.85365034738534]
  We introduce a unified framework for developing tool-augmented geospatial agents trained on satellite imagery, natural-language queries, and detailed reasoning traces.<n>The training pipeline relies on supervised fine-tuning over structured reasoning trajectories, aligning the model with verified multistep tool interactions.<n>The accompanying corpus comprises 14,538 training and 1,169 evaluation instances, with more than 100K reasoning steps in the training split and over 7K reasoning steps in the evaluation split.
  arXiv  Detail & Related papers  (2026-02-19T18:59:54Z)
• GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics [91.17301794848025]
  This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions.<n>Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies.
  arXiv  Detail & Related papers  (2026-02-13T04:48:05Z)
• Thinking with Geometry: Active Geometry Integration for Spatial Reasoning [68.59084007360615]
  We propose GeoThinker, a framework that shifts paradigm passive fusion to active perception.<n>Instead of feature mixing, GeoThinker enables the model to selectively retrieve geometric evidence conditioned on its internal reasoning demands.<n>Our results indicate that the ability to actively integrate spatial structures is essential for next-generation spatial intelligence.
  arXiv  Detail & Related papers  (2026-02-05T18:59:32Z)
• GeoGNN: Quantifying and Mitigating Semantic Drift in Text-Attributed Graphs [59.61242815508687]
  Graph neural networks (GNNs) on text--attributed graphs (TAGs) encode node texts using pretrained language models (PLMs) and propagate these embeddings through linear neighborhood aggregation.<n>This work introduces a local PCA-based metric that measures the degree of semantic drift and provides the first quantitative framework to analyze how different aggregation mechanisms affect manifold structure.
  arXiv  Detail & Related papers  (2025-11-12T06:48:43Z)
• Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations [4.302482081136594]
  We investigate the extent to which a well-known-text (WKT) representation of geometries and their spatial relations are preserved during spatial reasoning.<n>Our workflow employs three distinct approaches to complete the spatial reasoning tasks for comparison.
  arXiv  Detail & Related papers  (2025-05-22T05:21:31Z)
• Geometric Feature Enhanced Knowledge Graph Embedding and Spatial Reasoning [8.561588656662419]
  Geospatial Knowledge Graphs (GeoKGs) model geoentities and spatial relationships in an interconnected manner. Existing methods for mining and reasoning from GeoKGs, such as popular knowledge graph embedding (KGE) techniques, lack geographic awareness. This study aims to enhance general-purpose KGE by developing new strategies and integrating geometric features of spatial relations.
  arXiv  Detail & Related papers  (2024-10-24T00:53:48Z)
• Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching [60.645802236700035]
  Navigating drones through natural language commands remains challenging due to the dearth of accessible multi-modal datasets. We introduce GeoText-1652, a new natural language-guided geo-localization benchmark. This dataset is systematically constructed through an interactive human-computer process.
  arXiv  Detail & Related papers  (2023-11-21T17:52:30Z)
• SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation [25.52363878314735]
  SpaBERT provides a general-purpose geo-entity representation based on neighboring entities in geospatial data. SpaBERT is pretrained with masked language modeling and masked entity prediction tasks. We apply SpaBERT to two downstream tasks: geo-entity typing and geo-entity linking.
  arXiv  Detail & Related papers  (2022-10-21T19:42:32Z)
• Positional Encoder Graph Neural Networks for Geographic Data [1.840220263320992]
  Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. In this paper, we propose PE-GNN, a new framework that incorporates spatial context and correlation explicitly into the models.
  arXiv  Detail & Related papers  (2021-11-19T10:41:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.