论文标题
使用深度学习，卫星图像中的采矿和尾矿大坝检测
Mining and Tailings Dam Detection In Satellite Imagery Using Deep Learning
论文作者
论文摘要
这项工作探讨了自由云计算，免费开源软件以及深度学习方法的组合，以分析一个真实的大规模问题：巴西在全国范围内自动识别和分类地面矿山和采矿尾矿大坝。从巴西政府开放数据资源获得了正式注册矿山和大坝的位置。在Google Earth Engine平台获得和处理的多光谱Sentinel-2卫星图像用于使用Tensorflow 2 API和Google COLAB平台来训练和测试深神经网络。完全卷积的神经网络以创新的方式使用，以在巴西领土的大面积地区寻找未注册的矿山和尾坝。通过发现没有正式采矿特许权的263个矿山的发现证明了这种方法的功效。这项探索性工作突出了一套新技术的潜力，可自由使用，用于构建具有较高社会影响的低成本数据科学工具。同时，它讨论并试图为非法采矿的复杂而严重的问题和尾矿大坝的扩散提出实用的解决方案，这对人口和环境构成了很高的风险，尤其是在发展中国家。代码可公开提供：https：//github.com/remis/mining-discovery-with-deep-learning。
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.
