Environmental Monitoring Using Drone Images and Convolutional Neural Networks

Recently, drone images have been used in a number of applications, mainly for pollution control and surveillance purposes. In this paper, we introduce the well-known Convolutional Neural Networks in the context of environmental monitoring using drone images, and we show their robustness in real-world images obtained from uncontrolled scenarios. We consider a transfer learning-based approach and compare two neural models, i.e., VGG16 and VGG19, to distinguish four classes: “water”, “deforesting area”, “forest”, and “buildings”. The results are analyzed by experts in the field and considered pretty much reasonable.