Publication Type

Journal Article

Version

acceptedVersion

Publication Date

10-2024

Abstract

Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This article presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces both transductive (GeoNN-T) and inductive (GeoNN-I) models, ensuring effective encoding of geospatial features and scalability with entity changes. Extensive experiments demonstrate GeoNN's effectiveness in location-sensitive superpixel-based graphs and real-world points of interest, outperforming baselines across various evaluation measures.

Keywords

Geographic information systems, Data encoding and canonicalization, Data mining, Neural networks, Representation learning, Geospatial, Location sensitivity

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Spatial Algorithms and Systems

Volume

10

Issue

4

First Page

1

Last Page

31

ISSN

2374-0353

Identifier

10.1145/3663474

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3663474

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