This work explores the combination of free cloud computing, free open-source software, and deep learning methods to analyse 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 API and Google 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. Code is made publicly available at: https://github.com/remis/mining-discovery-with-deep-learning.
翻译:这项工作探索了免费云计算、免费开放源码软件和深层学习方法的结合,以分析一个真正的大规模问题:巴西境内地表地雷和尾矿水坝的自动全国性识别和分类;从巴西政府的开放数据资源中获得了正式登记的矿山和水坝的位置;谷歌地球引擎平台获得和处理的多光谱Sentinel-2卫星图像,用于利用TensorFlow 2 API和Google Colab平台来培训和测试深层神经网络;全面革命神经网络以创新方式使用,在巴西领土大片地区寻找未登记的矿山和尾矿坝;发现263个没有正式采矿特许权的矿山和水坝表明了这一方法的功效;这一探索性工作突出了一系列新技术的潜力,这些新技术可以免费获得,用于建造具有高度社会影响的低成本数据科学工具;同时,它讨论并寻求为非法采矿和尾矿坝扩散这一复杂而严重的问题提出切实可行的解决办法,这些问题对人口和环境构成高度风险,在发展中国家/正在公开学习。