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Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications.

Maria Despoina Siampou1, Jialiang Li2, John Krumm1

  • 1Department of Computer Science, University of Southern California, Los Angeles, USA.

Proceedings of Machine Learning Research
|April 16, 2026
PubMed
Summary
This summary is machine-generated.

Poly2Vec is a new method for encoding geospatial objects in geospatial artificial intelligence (GeoAI). This Fourier-based approach unifies various object types, preserving spatial properties for improved machine learning (ML) task performance.

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Area of Science:

  • Geospatial artificial intelligence (GeoAI)
  • Machine learning (ML)
  • Geospatial data representation

Background:

  • Encoding geospatial objects is crucial for GeoAI, but common methods lose vital spatial information.
  • Existing object-specific encoders lack versatility for diverse geospatial data types (points, polylines, polygons).

Purpose of the Study:

  • To introduce Poly2Vec, a unified, polymorphic encoding approach for geospatial objects.
  • To preserve essential spatial properties (topology, direction, distance) during encoding.
  • To enhance GeoAI workflows with a versatile and effective geospatial object representation.

Main Methods:

  • Developed Poly2Vec, a Fourier-based encoding method for unifying geospatial objects.
  • Incorporated a learned fusion module to adaptively integrate Fourier transform magnitude and phase.
  • Evaluated Poly2Vec on five diverse tasks, including spatial relationship preservation and GeoAI workflow integration.

Main Results:

  • Poly2Vec outperformed object-specific baselines in preserving topology, direction, and distance.
  • Integration of Poly2Vec improved performance in population prediction and land use inference tasks.
  • Demonstrated consistent superiority across diverse geospatial tasks and data types.

Conclusions:

  • Poly2Vec offers a robust and unified solution for geospatial object encoding in GeoAI.
  • The method effectively preserves critical spatial information lost in traditional approaches.
  • Poly2Vec enhances the performance and applicability of machine learning in geospatial analysis.