Gold Exploration using Representations from a Multispectral Autoencoder
This work addresses the high cost and limited data in mineral exploration by making it more efficient and scalable, though it is incremental as it builds on existing autoencoder and classifier methods.
The paper tackled the problem of identifying gold-bearing regions from satellite imagery by using generative representations from a pretrained autoencoder, improving patch-level accuracy from 0.51 to 0.68 and image-level accuracy from 0.55 to 0.73.
Satellite imagery is employed for large-scale prospectivity mapping due to the high cost and typically limited availability of on-site mineral exploration data. In this work, we present a proof-of-concept framework that leverages generative representations learned from multispectral Sentinel-2 imagery to identify gold-bearing regions from space. An autoencoder foundation model, called Isometric, which is pretrained on the large-scale FalconSpace-S2 v1.0 dataset, produces information-dense spectral-spatial representations that serve as inputs to a lightweight XGBoost classifier. We compare this representation-based approach with a raw spectral input baseline using a dataset of 63 Sentinel-2 images from known gold and non-gold locations. The proposed method improves patch-level accuracy from 0.51 to 0.68 and image-level accuracy from 0.55 to 0.73, demonstrating that generative embeddings capture transferable mineralogical patterns even with limited labeled data. These results highlight the potential of foundation-model representations to make mineral exploration more efficient, scalable, and globally applicable.