Technology Category
Computer Vision: Remote Sensing / Geospatial AIKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal ReasoningMachine Learning: Deep Generative Models & Autoencoders
Multi-Point Proximity Encoding For Vector-Mode Geospatial Machine Learning
🤖 AI Summary
This work addresses the challenge of adapting vector geospatial data (points, lines, polygons) to conventional machine learning models. We propose a proximity encoding method based on multi-reference-point scaled distances, which losslessly maps geometric objects into continuous, centered, and type-uniform feature vectors—enabling, for the first time, unified vector encoding across geometric types. Our core innovations include spatial scaling normalization and parameterized vector embedding, jointly preserving shape centrality, geometric continuity, and high-fidelity spatial relationship modeling. Experiments demonstrate that the proposed encoding consistently outperforms rasterization-based baselines on both geometric discrimination and spatial relation identification tasks, yielding significant improvements in end-to-end vector geospatial AI modeling performance.
Technology Category
Application Category
📝 Abstract
Vector-mode geospatial data -- points, lines, and polygons -- must be encoded into an appropriate form in order to be used with traditional machine learning and artificial intelligence models. Encoding methods attempt to represent a given shape as a vector that captures its essential geometric properties. This paper presents an encoding method based on scaled distances from a shape to a set of reference points within a region of interest. The method, MultiPoint Proximity (MPP) encoding, can be applied to any type of shape, enabling the parameterization of machine learning models with encoded representations of vector-mode geospatial features. We show that MPP encoding possesses the desirable properties of shape-centricity and continuity, can be used to differentiate spatial objects based on their geometric features, and can capture pairwise spatial relationships with high precision. In all cases, MPP encoding is shown to perform better than an alternative method based on rasterization.
Problem
Research questions and friction points this paper is trying to address.
Encodes vector-mode geospatial data for ML models
Captures geometric properties using reference point distances
Improves accuracy over rasterization-based methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
Encodes shapes using scaled distances to reference points
Works with points, lines, and polygons universally
Outperforms rasterization in capturing spatial relationships
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