{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T22:58:26Z","timestamp":1772837906267,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T00:00:00Z","timestamp":1607040000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o de Apoio \u00e0 Pesquisa do Distrito Federal - Brazil","award":["000"],"award-info":[{"award-number":["000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyze a real, large-scale problem: the automatic country-wide identification and classification of surface mines and mining tailings dams in Brazil. Locations of officially registered mines and dams were obtained from the Brazilian government open data resource. Multispectral Sentinel-2 satellite imagery, obtained and processed at the Google Earth Engine platform, was used to train and test deep neural networks using the TensorFlow 2 application programming interface (API) and Google Colaboratory (Colab) platform. Fully convolutional neural networks were used in an innovative way to search for unregistered ore mines and tailing dams in large areas of the Brazilian territory. The efficacy of the approach is demonstrated by the discovery of 263 mines that do not have an official mining concession. This exploratory work highlights the potential of a set of new technologies, freely available, for the construction of low cost data science tools that have high social impact. At the same time, it discusses and seeks to suggest practical solutions for the complex and serious problem of illegal mining and the proliferation of tailings dams, which pose high risks to the population and the environment, especially in developing countries.<\/jats:p>","DOI":"10.3390\/s20236936","type":"journal-article","created":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T11:59:00Z","timestamp":1607083140000},"page":"6936","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7442-2423","authenticated-orcid":false,"given":"Remis","family":"Balaniuk","sequence":"first","affiliation":[{"name":"Graduate Program in Governance, Technology and Innovation, Universidade Cat\u00f3lica de Bras\u00edlia, Bras\u00edlia 71966-700, Brazil"}]},{"given":"Olga","family":"Isupova","sequence":"additional","affiliation":[{"name":"Department Computer Science, University of Bath, Bath BA2 7PB, UK"}]},{"given":"Steven","family":"Reece","sequence":"additional","affiliation":[{"name":"Department Engineering Science, Oxford University, Oxford OX1 3PJ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,4]]},"reference":[{"key":"ref_1","unstructured":"ICOLD (2001). Tailings Dams\u2014Risk of Dangerous Occurrences, Lessons Learnt From Practical Experiences (Bulletin 121), Commission Internationale des Grands Barrages."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cameron, P.D., and Stanley, M.C. (2017). Oil, Gas, and Mining: A Sourcebook for Understanding the Extractive Industries, The World Bank.","DOI":"10.1596\/978-0-8213-9658-2"},{"key":"ref_3","unstructured":"Borges, A., and de S\u00e3o Paulo, O.E. (2014). TCU Revela Sonega\u00e7\u00e3o em \u00e1reas de Minera\u00e7\u00e3o, Estado de S\u00e3o Paulo. Available online: https:\/\/economia.estadao.com.br\/noticias\/geral,tcu-revela-sonegacao-em-areas-de-mineracao,1543471."},{"key":"ref_4","unstructured":"Reed, E., Miranda, M., and WWF Macroeconomics for Sustatinable Development Program Office (2020, November 25). Assessment of the Mining Sector and Infrastructure Development in the Congo Basin Region. Available online: http:\/\/awsassets.panda.org\/downloads\/congobasinmining.pdf."},{"key":"ref_5","unstructured":"Fellet, J., and Costa, C. (2019). Imagens Mostram Avan\u00e7O do Garimpo Ilegal na Amaz\u00f4nia em 2019, BBC News Brasil."},{"key":"ref_6","unstructured":"Guttag, J.V. (2016). Introduction to Computation and Programming Using Python: With Application to Understanding Data, MIT Press."},{"key":"ref_7","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_8","unstructured":"Atos do Poder Executivo (2020, November 25). DECRETO N\u00b0 9.406, DE 12 DE JUNHO DE 2018. DI\u00c1RIO OFICIAL DA UNI\u00c3O Publicado em: 13\/06\/2018|Edi\u00e7\u00e3o: 112|Se\u00e7\u00e3o: 1|P\u00e1gina: 1, Available online: http:\/\/www.planalto.gov.br\/ccivil_03\/_ato2015-2018\/2018\/decreto\/D9406.htm."},{"key":"ref_9","unstructured":"Minist\u00e9rio P\u00fablico Federal-Procuradoria da Rep\u00fablica no Estado de Minas Gerais (2020, November 25). A\u00e7\u00e3o Civil P\u00fablica com Pedido de Tutela Provis\u00f3ria de Urg\u00eancia em Face da UNI\u00c3O, Pessoa Jur\u00eddica de Direito p\u00faBlico Interno e da Ag\u00eancia Nacional de Minera\u00e7\u00e3o. Available online: http:\/\/www.mpf.mp.br\/mg\/sala-de-imprensa\/docs\/acp_anm_uniao-1."},{"key":"ref_10","unstructured":"Sales, C.D. (2018). Licenciamento Ambiental da Atividade de Minera\u00e7\u00e3o em Unidades de Conserva\u00e7\u00e3o no Amazonas: Incid\u00eaNcia, Suporte Jur\u00edDico Administrativo e Aperfei\u00e7oamentos. [Master\u2019s Thesis, INPA]."},{"key":"ref_11","unstructured":"WWF-Brasil (2020, November 25). Minera\u00e7\u00e3o na Amaz\u00f4nia Legal e \u00c1reas Protegidas-Situa\u00e7\u00e3o dos Direitos Miner\u00e1rios e Sobreposi\u00e7\u00f5es. Available online: https:\/\/www.wwf.org.br\/?67842%2FMineracao-em-areas-protegidas-titulos-sao-risco-potencial-diz-estudo-do-WWF-Brasil."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Legg, C.A. (1990). Applications of remote sensing to environmental aspects of surface mining operations in the United Kingdom. Remote Sensing: An Operational Technology for the Mining and Petroleum Industries, Springer.","DOI":"10.1007\/978-94-010-9744-4_17"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.oregeorev.2018.08.019","article-title":"Monitoring surface mining belts using multiple remote sensing datasets: A global perspective","volume":"101","author":"Yu","year":"2018","journal-title":"Ore Geol. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.catena.2016.03.017","article-title":"Changes in land use due to mining in the north-western mountains of Spain during the previous 50 years","volume":"149","year":"2017","journal-title":"CATENA"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/10106049.2012.706648","article-title":"Change detection of surface mining activity and reclamation based on a machine learning approach of multi-temporal Landsat TM imagery","volume":"28","author":"Petropoulos","year":"2013","journal-title":"Geocarto Int."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Connette, K.L., Connette, G., Bernd, A., Phyo, P., Aung, K., Tun, Y., Thein, Z., Horning, N., Leimgruber, P., and Songer, M. (2016). Assessment of Mining Extent and Expansion in Myanmar Based on Freely-Available Satellite Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8110912"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1002\/ldr.2412","article-title":"Mapping Extent and Change in Surface Mines Within the United States for 2001 to 2006","volume":"27","author":"Soulard","year":"2015","journal-title":"Land Degrad. Dev."},{"key":"ref_18","unstructured":"Prasad, S. (2015). Remotely Sensed Data Characterization, Classification, and Accuracies, CRC Press. Chapter Processing Remote-Sensing Data in Cloud Computing Environments."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Penatti, O.A., Nogueira, K., and Dos Santos, J.A. (2015, January 7\u201312). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TGRS.2016.2616585","article-title":"Dense semantic labeling of subdecimeter resolution images with convolutional neural networks","volume":"55","author":"Volpi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1109\/LGRS.2019.2903194","article-title":"Spatio-Temporal Vegetation Pixel Classification By Using Convolutional Networks","volume":"16","author":"Nogueira","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xie, M., Jean, N., Burke, M., Lobell, D., and Ermon, S. (2015). Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. arXiv.","DOI":"10.1609\/aaai.v30i1.9906"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mazzia, V., Comba, L., Khaliq, A., Chiaberge, M., and Gay, P. (2020). UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture. Sensors, 20.","DOI":"10.3390\/s20092530"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Saraiva, M., Protas, E., Salgado, M., and Souza, C. (2020). Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning. Remote Sens., 12.","DOI":"10.3390\/rs12030558"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gu, Y., Wang, Y., and Li, Y. (2019). A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection. Appl. Sci., 9.","DOI":"10.3390\/app9102110"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sensors"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1109\/LGRS.2017.2763738","article-title":"Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images","volume":"14","author":"Persello","year":"2017","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ferreira, E., Brito, M., Alvim, M.S., Balaniuk, R., and dos Santos, J. (2020, January 22\u201326). BrazilDAM: A Benchmark dataset for Tailings Dam Detection. Proceedings of the Latin American GRSS ISPRS Remote Sensing Conference, Santigo, Chile.","DOI":"10.1109\/LAGIRS48042.2020.9165620"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote. Sens. Environ.","DOI":"10.1016\/j.rse.2017.06.031"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A Large-Scale Hierarchical Image Database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_33","unstructured":"Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems 25, Curran Associates, Inc."},{"key":"ref_34","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer International Publishing.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, H., Ellis, J.G., Zhang, L., and Chang, S. (2018, January 11\u201314). PatternNet: Visual Pattern Mining with Deep Neural Network. Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, Yokohama, Japan.","DOI":"10.1145\/3206025.3206039"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"61677","DOI":"10.1109\/ACCESS.2018.2874767","article-title":"Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications","volume":"6","author":"Carneiro","year":"2018","journal-title":"IEEE Access"},{"key":"ref_39","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations. arXiv."},{"key":"ref_40","unstructured":"Wang, P., and Kvalheim, A. (1994). Environmental Impact Assessment (EIA) of Development Aid Projects. Initial Environmental Assessment, Technical Report."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6936\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:41:30Z","timestamp":1760179290000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/23\/6936"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,4]]},"references-count":41,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["s20236936"],"URL":"https:\/\/doi.org\/10.3390\/s20236936","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,4]]}}}