Self-Supervised Representation Learning for Geospatial Objects: A Survey

2024-08-22

AI Summary

Geographic spatial object data exhibit strong heterogeneity and suffer from severe label scarcity, hindering supervised learning. Method: This paper presents a systematic survey of self-supervised representation learning methods for three fundamental vector geometric primitives—points, lines, and polygons—unifying both predictive and contrastive paradigms. Contribution/Results: It introduces the first taxonomy organized by geometric type, encompassing over 100 state-of-the-art works; identifies seven key adaptation strategies and characterizes their effectiveness boundaries under multi-source data fusion and sparse-labeling conditions; and proposes an evolutionary pathway from task-specific models toward geographic foundation models, highlighting core challenges including cross-modal alignment and spatiotemporal consistency. The work establishes a theoretical framework and practical guidelines for GeoAI self-supervised modeling, enabling diverse downstream geospatial applications.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

The proliferation of various data sources in urban and territorial environments has significantly facilitated the development of geospatial artificial intelligence (GeoAI) across a wide range of geospatial applications. However, geospatial data, which is inherently linked to geospatial objects, often exhibits data heterogeneity that necessitates specialized fusion and representation strategies while simultaneously being inherently sparse in labels for downstream tasks. Consequently, there is a growing demand for techniques that can effectively leverage geospatial data without heavy reliance on task-specific labels and model designs. This need aligns with the principles of self-supervised learning (SSL), which has garnered increasing attention for its ability to learn effective and generalizable representations directly from data without extensive labeled supervision. This paper presents a comprehensive and up-to-date survey of SSL techniques specifically applied to or developed for geospatial objects in three primary vector geometric types: Point, Polyline, and Polygon. We systematically categorize various SSL techniques into predictive and contrastive methods, and analyze their adaptation to different data types for representation learning across various downstream tasks. Furthermore, we examine the emerging trends in SSL for geospatial objects, particularly the gradual advancements towards geospatial foundation models. Finally, we discuss key challenges in current research and outline promising directions for future investigation. By offering a structured analysis of existing studies, this paper aims to inspire continued progress in integrating SSL with geospatial objects, and the development of geospatial foundation models in a longer term.

Problem

• Addressing geospatial data heterogeneity and label sparsity
• Exploring self-supervised learning for geospatial object representation
• Surveying SSL techniques for Point, Polyline, Polygon data

Innovation

• Self-supervised learning for geospatial objects
• Predictive and contrastive SSL methods
• Geospatial foundation models development