Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models
2026-06-01 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors explain that Earth Observation uses images and data to monitor the planet, but current models only focus on image-like raster data and ignore vector data, which includes detailed information about objects and their relationships. They argue that combining both raster images and vector data in one learning system would give a fuller understanding of geography. This unified approach would help create better AI tools that can understand physical landscapes and human-made features together. The paper discusses why this combination is challenging and suggests ways to achieve it.
Earth ObservationRaster DataVector DataSelf-Supervised LearningSpatial Representation LearningGeospatial AIOpenStreetMapMultimodal LearningGeographic Information SystemsEmbedding Space
Authors
Steffen Knoblauch, Hao Li, Gengchen Mai, Konstantin Klemmer, Song Gao, WenWen Li
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.