A LSTM-UNet and Zero Padding technique to detect deforestation in Amazon area

Abstract

The Amazon Rainforest is the largest forest in the world that stores various kinds of biodiversity, both flora and fauna. The protection of the integrity and sustainability of this rainforest is a concern for the entire international community. One form of protection is by mapping the deforestation areas by using deep learning. This paper proposes a novel Deep Learning method that combines U-Net with LSTM and Zero Padding in each convolution layer in U-net to map deforestation areas. Boundary between deforested and non-deforested area is made to boost the overall precision of the model. Generally, the proposed method indicates good accuracy in mapping the deforestation areas, which is 93.35% with an F1-score of 93.82% and a low loss value of 0.1654, while boundary use slightly boosted the overall precision into 94.06% because the use of boundaries aims to limit areas with very narrow class differences.