Synthetic Reflections on Resource Extraction

TL;DR

This paper presents a framework combining statistical, human, and generative AI methods to interpret satellite landscapes, introducing a new index to assess mining sites globally.

Contribution

It introduces the Urban Dwelling and Mining Index and demonstrates its use in enhancing multimodal language models for mining site analysis.

Findings

The new index improves model assessment of mining spatial distribution.

A pipeline integrating multiple AI techniques effectively interprets satellite imagery.

The framework enables succinct commentary generation on industrial sites worldwide.

Abstract

This paper describes how AI models can be augmented and adapted to interpret landscapes. We present the technical framework of a Sentinel-2 satellite asset interpretation pipeline that combines statistical operations, human judgment, and generative AI models to produce succinct commentaries on industrial mining sites across the planet. To this end we introduce a novel bespoke landscape descriptor, the Urban Dwelling and Mining Index, and discuss how this metric can improve the performance of a multimodal language model in assessing the spatial distribution of mining operations.