GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data

• URL: http://arxiv.org/abs/2509.26016v1
• Date: Tue, 30 Sep 2025 09:45:52 GMT
• Title: GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data
• Authors: Lubian Bai, Xiuyuan Zhang, Siqi Zhang, Zepeng Zhang, Haoyu Wang, Wei Qin, Shihong Du,
• Abstract summary: This study presents GeoLink, a framework that leverages OpenStreetMap (OSM) data to enhance remote sensing (RS) foundation models (FMs)<n> Specifically, GeoLink enhances RS self-supervised pretraining using multi-granularity learning signals derived from OSM data.<n>For downstream tasks, GeoLink generates both unimodal and multimodal fine-grained encodings to support a wide range of applications.
• Score: 13.450535648972682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrating ground-level geospatial data with rich geographic context, like OpenStreetMap (OSM), into remote sensing (RS) foundation models (FMs) is essential for advancing geospatial intelligence and supporting a broad spectrum of tasks. However, modality gap between RS and OSM data, including differences in data structure, content, and spatial granularity, makes effective synergy highly challenging, and most existing RS FMs focus on imagery alone. To this end, this study presents GeoLink, a multimodal framework that leverages OSM data to enhance RS FM during both the pretraining and downstream task stages. Specifically, GeoLink enhances RS self-supervised pretraining using multi-granularity learning signals derived from OSM data, guided by cross-modal spatial correlations for information interaction and collaboration. It also introduces image mask-reconstruction to enable sparse input for efficient pretraining. For downstream tasks, GeoLink generates both unimodal and multimodal fine-grained encodings to support a wide range of applications, from common RS interpretation tasks like land cover classification to more comprehensive geographic tasks like urban function zone mapping. Extensive experiments show that incorporating OSM data during pretraining enhances the performance of the RS image encoder, while fusing RS and OSM data in downstream tasks improves the FM's adaptability to complex geographic scenarios. These results underscore the potential of multimodal synergy in advancing high-level geospatial artificial intelligence. Moreover, we find that spatial correlation plays a crucial role in enabling effective multimodal geospatial data integration. Code, checkpoints, and using examples are released at https://github.com/bailubin/GeoLink_NeurIPS2025

Related papers

• GRASP: Guided Region-Aware Sparse Prompting for Adapting MLLMs to Remote Sensing [50.961694646995376]
  We propose a parameter-efficient fine-tuning (PEFT) strategy called Guided Region-Aware Sparse Prompting (GRASP)<n>GRASP introduces spatially structured soft prompts associated with spatial blocks extracted from a frozen visual token grid.<n>Experiments on multiple RSVQA benchmarks show that GRASP achieves competitive performance compared to existing fine-tuning and prompt-based methods.
  arXiv  Detail & Related papers  (2026-01-23T10:12:59Z)
• Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagery [10.20881592893918]
  Self-supervised learning (SSL) has become a powerful paradigm for learning from large, unlabeled datasets.<n>Applying SSL to multispectral remote sensing (RS) images presents unique challenges and opportunities due to the geographical and temporal variability of the data.<n>We introduce GeoRank, a novel regularization method for contrastive SSL that improves upon prior techniques.
  arXiv  Detail & Related papers  (2026-01-05T17:24:50Z)
• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• UrbanMIMOMap: A Ray-Traced MIMO CSI Dataset with Precoding-Aware Maps and Benchmarks [33.93643630910854]
  Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC)<n>generating high-fidelity RM ground truth via electromagnetic (EM) simulations is computationally intensive, motivating machine learning (ML)-based RM construction.<n>We present UrbanMIMOMap, a novel large-scale urban multi-input multi-output (MIMO) dataset generated using high-precision ray tracing.
  arXiv  Detail & Related papers  (2025-09-08T01:23:46Z)
• GAIR: Improving Multimodal Geo-Foundation Model with Geo-Aligned Implicit Representations [5.439918212297177]
  Key challenge in multimodal GeoFM development is to explicitly model geospatial relationships across modalities.<n>We propose GAIR, a novel multimodal GeoFM architecture integrating overhead RS data, street view imagery, and their geolocation metadata.<n>We evaluate GAIR across 10 geospatial tasks spanning RS image-based, SV image-based, and location embedding-based benchmarks.
  arXiv  Detail & Related papers  (2025-03-20T19:59:39Z)
• OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence [51.0456395687016]
  multimodal large language models (LLMs) have opened new frontiers in artificial intelligence.<n>We propose a MLLM (OmniGeo) tailored to geospatial applications.<n>By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems.
  arXiv  Detail & Related papers  (2025-03-20T16:45:48Z)
• Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data [14.104497777255137]
  We introduce Low-rank Efficient Spatial-Spectral Vision Transformer with three key innovations.<n>We pretrain LESS ViT using a Hyperspectral Masked Autoencoder framework with integrated positional and channel masking strategies.<n> Experimental results demonstrate that our proposed method achieves competitive performance against state-of-the-art multi-modal geospatial foundation models.
  arXiv  Detail & Related papers  (2025-03-17T05:42:19Z)
• Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
  We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-21T03:31:30Z)
• Federated Multi-Agent Mapping for Planetary Exploration [0.4143603294943439]
  We propose a federated multi-agent mapping approach that jointly trains a global map model across agents without transmitting raw data.<n>Our method leverages implicit neural mapping to generate parsimonious, adaptable representations, reducing data transmission by up to 93.8% compared to raw maps.<n>We demonstrate the efficacy of our approach on Martian terrains and glacier datasets, achieving downstream path planning F1 scores as high as 0.95 while outperforming on map reconstruction losses.
  arXiv  Detail & Related papers  (2024-04-02T20:32:32Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• GeoLLM: Extracting Geospatial Knowledge from Large Language Models [49.20315582673223]
  We present GeoLLM, a novel method that can effectively extract geospatial knowledge from large language models. We demonstrate the utility of our approach across multiple tasks of central interest to the international community, including the measurement of population density and economic livelihoods. Our experiments reveal that LLMs are remarkably sample-efficient, rich in geospatial information, and robust across the globe.
  arXiv  Detail & Related papers  (2023-10-10T00:03:23Z)

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.