Identification of Gold Mining Vessels based on Classification Algorithms using Sentinel-2 Images

• Autor(a) principal: Pereira, Diego Henrique Costa
• Data de Publicação: 2023
• Outros Autores: Gomes, Roberto Arnaldo Trancoso, Carvalho Júnior, Osmar Abílio de, Guimarães, Renato Fontes
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Sociedade & natureza (Online)
• Texto Completo: https://seer.ufu.br/index.php/sociedadenatureza/article/view/69409
• topic: Garimpos de Ouro Embarcados; Classificadores; Machine learning; Gold mining vessels; Classifiers; Machine learning

Artisanal and small-scale gold mining can occur on land or in riverbeds. However, the activity needs to be supported by a Mining Permit, issued by the Agência Nacional de Mineração, and the appropriate environmental license from the competent environmental agency. The use of images from Sentinel-2 satellites presents itself as a potential tool for identifying gold mining vessels due to the temporal resolution, free imagery, global coverage, and more refined spatial resolution. So, this study aimed to identify gold mining vessels on the Madeira River near Porto Velho city, Rondônia state, located at Brazilian Amazon, in 13 Sentinel-2 images from 2018 to 2021 using the classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN) Random Forest (RF) and Spectral Angle Mapper (SAM). The results showed that machine learning classifiers obtained the best performance, especially the object-oriented SVM classifier, which had the best average F1 score (0.91).  In addition, the detection percentage of gold mining vessels originated by this classifier was satisfactory, with only 0 to 4 active gold mining vessels with sediment plumes being omitted per image. Therefore, based on the results obtained, it was concluded that the use of machine learning classifiers proved to be effective in identifying gold mining vessels.