Spontaneous combustion of coal is a critical problem encountered by mining and thermal power industries. Usually, coal is stored in open area in the form of stockpiles in the coal mines and thermal power plants. In this paper, we have focused on localization of open-cast coal mines and coal stockpiles using satellite images to automate the entire process of prediction of spontaneous combustion in the coal stockpiles. We have used USGS Landsat-8 Satellite images, collected from various coal mines and thermal plants across the world. The satellite images consist of 11 bands including Red, Blue, Green, Near Infrared (NIR), and Shortwave Infrared (SWIR). Apart from the reflectance measurements obtained from these bands, we also use standard indices including Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI) and Normalized Difference Water Index (NDWI) as the features to train the models. The ground truth for the training dataset has been created by manually annotating these images for three classes: coal mines, coal stockpiles and water bodies. The Fully Convolutional Network (FCN) based U-Net architecture has been trained to develop two models to classify pixels between (A) Coal Mine and Water and (B) Coal Stockpile and Water. In this paper, we present an exhaustive experimental results to demonstrate the effective localization of coal mine and coal stockpiles using the proposed FCN based approach.