Deforestation Detection Using Deep Learning

Deforestation Detection Using Deep Learning

Manthan Surjuse, Eshan Pandhare, Tanmay Sonule, H.Y Nawale

Department of Electronics and

Computer Engineering, PES MCOE, Pune, Maharashtra, India

Abstract: This is a major environmental problem that affects many countries around the world” (World, 2020). In this paper, we have explored several methods of automatic method for deforestation detection such as Early Fusion Convolutional network (EFCN), Siamese Convolutional Network (S-CNN) and Support Vector Machine (SVM). As shown in all experimental results, EFCN can obviously outperform S-CNN and SVM. This work is aimed at presenting a novel curated dataset and also an approach based on deep learning, more specifically Convolutional Neural Networks (CNN) combined with cutting edge data processing techniques to solve the forestation problem. These tools, combined with more advanced deep learning models and higher resolution satellite imagery, have greatly expanded our ability to do this. Finally, this paper explains a tool for daily the detection of rainforests deforestation in satellite images from MODIS/TERRA sensor using Artificial Neural Networks and U-net architectures. “What Comes to Mind When Considering Deforestation. Image, satellite images, deep learning, and CNN (Convolutional Neural Network).”