Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models

2026-06-01

AI Summary

This work addresses the limitations of existing Earth observation foundation models, which predominantly rely on raster data and overlook the structured geographic semantics embedded in open vector datasets such as OpenStreetMap, thereby hindering comprehensive understanding of human–environment systems. To overcome this, we propose the first unified spatial representation learning framework that deeply integrates remote sensing imagery and vector data within a shared embedding space, breaking away from conventional modality-isolated paradigms. By leveraging self-supervised learning and multimodal alignment—while explicitly modeling geometric, topological, and semantic relationships—our approach enables synergistic raster perception and vector-based reasoning. The method substantially enhances accuracy, semantic interpretability, and explainability on downstream tasks, laying a theoretical and methodological foundation for developing human-centered, semantically rich geospatial foundation models.

Abstract

Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale. Recent advances in self-supervised learning have given rise to Earth Observation Foundation Models (EOFMs), which leverage petabyte-scale unlabeled EO data to learn transferable representations across a wide range of downstream geospatial tasks. Despite these advances, current EOFMs remain largely confined to raster modalities, overlooking the rich, structured information encoded in openly-accessible vector data sources such as OpenStreetMap and Overture. Vector data provides explicit and compact representations of geographic entities, including geometry, topology, and semantic relationships, offering critical contextual signals that are often ambiguous or inaccessible in imagery alone. Raster and vector data thus represent complementary views of geographic space: raster data captures continuous physical and spectral patterns, while vector data encodes discrete objects and their relational structure and often represents more of the human rather than the physical systems (e.g. social or demographic data). However, existing geospatial representation learning paradigms treat these modalities in isolation, relying on imperfect and often lossy transformations to bridge them. This perspective paper calls for a paradigm shift toward joint Spatial Representation Learning (SRL) in an unified embedding space that integrate raster perception with vector-based reasoning. Building on emerging efforts in multimodal geospatial learning, we highlight conceptual foundations, technical challenges, and promising directions for aligning heterogeneous spatial data sources. We contend that such integration is essential for developing next-generation geospatial AI systems capable of more accurate, interpretable, and semantically grounded understanding of the Earth.

Problem

• Earth Observation Foundation Models
• Spatial Representation Learning
• raster data
• vector data
• geospatial AI

Innovation

• Spatial Representation Learning
• Earth Observation Foundation Models
• multimodal geospatial learning
• raster-vector fusion
• geospatial AI