Gold Exploration using Representations from a Multispectral Autoencoder

• URL: http://arxiv.org/abs/2602.06748v1
• Date: Fri, 06 Feb 2026 14:47:12 GMT
• Title: Gold Exploration using Representations from a Multispectral Autoencoder
• Authors: Argyro Tsandalidou, Konstantinos Dogeas, Eleftheria Tetoula Tsonga, Elisavet Parselia, Georgios Tsimiklis, George Arvanitakis,
• Abstract summary: We present a proof-of-concept framework that leverages generative representations learned from Sentinel-2 imagery to identify gold-bearing regions from space.<n>An autoencoder foundation model, called Isometric, is pretrained on the large-scale FalconSpace-S2 v1.0 dataset.<n>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.
• Score: 0.26385121748044166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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