Abstract
ABSTRACT Coal is a principal source of energy and the combustion of coal supplies around one-third of the global electricity generation. Coal mines are also an important source of CH4 emissions, the second most important greenhouse gas. Monitoring CH4 emissions caused by coal mining using earth observation will require the exact location of coal mines. This paper aims to determine surface coal mines from satellite images through deep learning techniques by treating them as a land use/land cover classification task. This is achieved using Convolutional Neural Networks (CNN) that has proven to be capable of complex land use/land cover classification tasks. With a list of known coal mine locations from various countries, a training dataset of “Coal Mine” and “No Coal Mine” image patches is prepared using Sentinel-2 satellite images with 13 spectral bands. Various pre-trained CNN network architectures (VGG, ResNet, DenseNet) are trained and validated with our prepared coal mine dataset of 3500 “Coal Mine” and 3000 “No Coal Mine” image patches. After several experiments with the VGG network combined with transfer learning is found to be an optimal model for this task. Classification accuracy of 98% has been achieved for the validation dataset of the pre-trained VGG architecture. The model produces more than 95% overall accuracy when tested on unseen satellite images from different countries outside the training dataset and evaluated against visual classification.
Highlights
Methane (CH4) is the second most important anthro pogenic greenhouse gas after carbon dioxide, with a Global Warming Potential (GWP) of 84 compared to CO2 over a 20-year time horizon (Myhre et al, 2013)
Both Residual Learning network (ResNet) and DenseNet have a larger number of layers and a deeper architecture than VGG. These three models are chosen based on many other image classification studies and their per formance in it. Whereas this led to increased compu tational time for ResNet and DenseNet, there is no noticeable improvement in accuracy to justify the increased computational costs
For testing the model performance on unseen satellite images, Sentinel-2 tiles with coal mines are selected from different countries and classi fied with the trained model
Summary
Methane (CH4) is the second most important anthro pogenic greenhouse gas after carbon dioxide, with a Global Warming Potential (GWP) of 84 compared to CO2 over a 20-year time horizon (Myhre et al, 2013). Due to this high warming potential on a short time scale, reducing methane emissions is considered an essential and relatively achievable task to miti gate climate change in the near future. Most scholars suggest that the contribution of fossil fuel-related methane emissions is highly under estimated (Kholod et al, 2019). The emission estimates and associated mitigation potential vary based on mine type, coal type and mining prac tices from country to country
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