Detecting illegal gold mining sites in the Amazon forest : Using Deep Learning to Classify Satellites Images

Abstract: Illegal gold mining in the Amazon forest has increased dramatically since 2005 with the rise in the price of gold. The use of chemicals such as mercury, which facilitate the gold extraction, increases the toxicity of the soil and can enter the food chain, leading to health problems for the inhabitants, and causes the environmental scourge we know today. In addition, the massive increase in these activities favours deforestation and impacts on protected areas such as indigenous areas and natural reserves. Organisations and governments from Peru, Brazil and French Guyana in particular, are trying to regulate these activities, but the area to cover being very large, by the time illegal exploitation is detected it is often too late to react. The idea of this thesis is to evaluate whether it is possible to automate the task of detecting these illegal gold mines using open satellite images and deep learning. In order to answer this question, this report includes the creation of new datasets, as well as the evaluation of two techniques which are object detection using RetinaNet and semantic segmentation using U-Net. The influence of image spectral bands is also studied in this thesis. The numerous trained models are all evaluated using the Dice Coefficient and Intersection over Union metrics, and each comparison is supported by the statistical sign-test. The report shows the superiority of the segmentation model for the binary classification of illegal mines. However, it is suggested to first use RetinaNet to find out more precisely whether the mine is legal or illegal, and then to use U-Net if the mine is illegal in order to make a more precise segmentation. It also shows and illustrates the importance of using the right image spectral bands which greatly increases the accuracy of the models.