Ofori-Ampofo, Stella und Zappacosta, Antony und Kuzu, Ridvan Salih und Schauer, Peter und Willberg, Martin und Zhu, Xiao Xiang (2025) SmallMinesDS: A Multimodal Dataset for Mapping Artisanal and Small-Scale Gold Mines. IEEE Geoscience and Remote Sensing Letters, 22, Seite 2502705. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2025.3566356. ISSN 1545-598X.
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Offizielle URL: https://ieeexplore.ieee.org/abstract/document/10982207
Kurzfassung
The increasing demand for gold, coupled with persistently high market prices over the past decade, has driven a significant rise in small-scale gold production. The expansion of unregularized small-scale gold mines fuels environmental degradation and poses a risk to miners and mining communities. To promote sustainable mining practices, support reclamation initiatives, and pave the way for understudying the impacts of mining on human and environmental resources, we present SmallMinesDS, a benchmark dataset derived from multisensor satellite imagery covering five districts in southwestern Ghana in two time periods. SmallMinesDS provides precise reference data for artisanal mining sites, enabling the development of machine learning models for timely, large-scale, and cost-effective monitoring. Notably, foundation models (FMs) fine-tuned on SmallMinesDS achieve up to 75% intersection over union while maintaining a strong balance between minimizing false positives and negatives.
| elib-URL des Eintrags: | https://elib.dlr.de/221107/ | ||||||||||||||||||||||||||||
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| Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||
| Zusätzliche Informationen: | This work was supported in part by the FAST-EO Project funded by European Space Agency (ESA) under Contract 4000143501/23/I-DT and in part by the Munich Aerospace e.V. Scholarship. | ||||||||||||||||||||||||||||
| Titel: | SmallMinesDS: A Multimodal Dataset for Mapping Artisanal and Small-Scale Gold Mines | ||||||||||||||||||||||||||||
| Autoren: |
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| Datum: | 2 Mai 2025 | ||||||||||||||||||||||||||||
| Erschienen in: | IEEE Geoscience and Remote Sensing Letters | ||||||||||||||||||||||||||||
| Referierte Publikation: | Ja | ||||||||||||||||||||||||||||
| Open Access: | Ja | ||||||||||||||||||||||||||||
| Gold Open Access: | Nein | ||||||||||||||||||||||||||||
| In SCOPUS: | Ja | ||||||||||||||||||||||||||||
| In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||
| Band: | 22 | ||||||||||||||||||||||||||||
| DOI: | 10.1109/LGRS.2025.3566356 | ||||||||||||||||||||||||||||
| Seitenbereich: | Seite 2502705 | ||||||||||||||||||||||||||||
| Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||||||||||
| ISSN: | 1545-598X | ||||||||||||||||||||||||||||
| Status: | veröffentlicht | ||||||||||||||||||||||||||||
| Stichwörter: | Earth observation, Foundation models (FMs), Machine learning, Mining, Semantic segmentation | ||||||||||||||||||||||||||||
| HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||
| HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||||||
| HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||||||
| DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||||||
| DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||||||
| DLR - Teilgebiet (Projekt, Vorhaben): | R - Optische Fernerkundung, R - Fernerkundung u. Geoforschung | ||||||||||||||||||||||||||||
| Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||||||
| Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science Institut für Methodik der Fernerkundung | ||||||||||||||||||||||||||||
| Hinterlegt von: | Kuzu, Dr. Ridvan Salih | ||||||||||||||||||||||||||||
| Hinterlegt am: | 09 Jan 2026 10:09 | ||||||||||||||||||||||||||||
| Letzte Änderung: | 09 Jan 2026 10:09 |
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