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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.06748 |
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| _version_ | 1866915780207050752 |
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| author | Tsandalidou, Argyro Dogeas, Konstantinos Tsonga, Eleftheria Tetoula Parselia, Elisavet Tsimiklis, Georgios Arvanitakis, George |
| author_facet | Tsandalidou, Argyro Dogeas, Konstantinos Tsonga, Eleftheria Tetoula Parselia, Elisavet Tsimiklis, Georgios Arvanitakis, George |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06748 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Gold Exploration using Representations from a Multispectral Autoencoder Tsandalidou, Argyro Dogeas, Konstantinos Tsonga, Eleftheria Tetoula Parselia, Elisavet Tsimiklis, Georgios Arvanitakis, George Computer Vision and Pattern Recognition Artificial Intelligence 68T05 I.2.6 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. |
| title | Gold Exploration using Representations from a Multispectral Autoencoder |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T05 I.2.6 |
| url | https://arxiv.org/abs/2602.06748 |