Self-Supervised Representation Learning for Geospatial Objects: A Survey

• URL: http://arxiv.org/abs/2408.12133v2
• Date: Fri, 25 Apr 2025 06:06:00 GMT
• Title: Self-Supervised Representation Learning for Geospatial Objects: A Survey
• Authors: Yile Chen, Weiming Huang, Kaiqi Zhao, Yue Jiang, Gao Cong,
• Abstract summary: Self-supervised learning (SSL) has garnered increasing attention for its ability to learn effective and generalizable representations directly from data without extensive labeled supervision.<n>This paper presents a survey of SSL techniques specifically applied to or developed for geospatial objects in three primary geometric vector types: Point, Polyline, and Polygon.<n>We examine the emerging trends in SSL for geospatial objects, particularly the gradual advancements towards geospatial foundation models.
• Score: 21.504978593542354
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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