GeoAI in resource-constrained environments

TL;DR

This paper explores GeoAI tailored for small organizations with limited resources, addressing challenges of data scarcity and compute constraints, and discusses future impacts of large geospatial models on landscape representation.

Contribution

It introduces strategies for resource-efficient GeoAI and considers future implications of large models in resource-constrained environments.

Findings

Strategies for resource-efficient GeoAI deployment

Potential homogenization of landscape representation by large models

Preparation approaches for future geospatial AI challenges

Abstract

This paper describes spatially aware Artificial Intelligence, GeoAI, tailored for small organizations such as NGOs in resource constrained contexts where access to large datasets, expensive compute infrastructure and AI expertise may be restricted. We furthermore consider future scenarios in which resource-intensive, large geospatial models may homogenize the representation of complex landscapes, and suggest strategies to prepare for this condition.