arXiv:2606.24997v1 Announce Type: new Abstract: Geographic implicit neural representations (INRs) learn to map any coordinate on Earth to a location embedding, implicitly encoding geospatial data into the weights of a neural network. Location embeddings are widely used off the shelf as general-purpose geospatial representations, yet users lack principled tools to audit what geographic or semantic information these embeddings capture. In this work, we analyze the information content of geographic INRs through their location embeddings. We decompose these embeddings into human-interpretable feat
The proliferation of geographic implicit neural representations (INRs) as general-purpose geospatial tools necessitates deeper understanding of their internal mechanisms and biases, particularly as they integrate into critical systems.
Understanding how AI models encode and interpret real-world geographic information is crucial for ensuring accuracy, identifying biases, and establishing trust in critical applications ranging from logistics to environmental analysis.
This work introduces a methodology to decompose and explain the information embedded within location encoders, moving beyond black-box usage to principled auditing of geospatial AI representations.
- · AI explainability researchers
- · Geospatial AI developers
- · Urban planning
- · Environmental science
- · Developers relying solely on black-box geospatial models
- · Regions or demographics poorly represented in training data
Improved trust and reliability in AI systems that depend on geographic data due to increased transparency.
Development of regulatory standards or best practices for explainability in geospatial AI, influencing model design and deployment.
More equitable and less biased AI applications in areas like resource allocation or infrastructure development, by allowing for the identification and correction of geographic biases.
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Read at arXiv cs.LG