Deforestation Detection in the Amazon Rainforest With Spatial and Channel Attention Mechanisms

Abstract

Abstract. Deforestation in the Amazon rainforest is an alarming problem of global interest. Environmental impacts of this process are countless, but probably the most significant concerns regard the increase in CO2 emissions and global temperature rise. Currently, the assessment of deforested areas in the Amazon region is a manual task, where people analyse multiple satellite images to quantify the deforestation. We propose a method for automatic deforestation detection based on Deep Learning Neural Networks with dual-attention mechanisms. We employed a siamese architecture to detect deforestation changes between optical images in 2018 and 2019. Experiments were performed to evaluate the relevance and sensitivity of hyperparameter tuning of the loss function and the effects of dual-attention mechanisms (spatial and channel) in predicting deforestation. Experimental results suggest that a proper tuning of the loss function might bring benefits in terms of generalisation. We also show that the spatial attention mechanism is more relevant for deforestation detection than the channel attention mechanism. When both mechanisms are combined, the greatest improvements are found, and we reported an increase of 1.06% in the mean average precision over a baseline.

Estudo Comparativo da Detecção de Desmatamento em Cenas Sentinel-1 da Floresta Amazônica

“…Tendo como referência as marcac ¸ões ground-truth das imagens que mostram as áreas de desmatamento confirmado, analisamos o desempenho dos participantes e do modelo calculando as seguintes métricas: precisão, F1 (média harmônica entre a precisão e recall), intersecc ¸ão sobre a união (IOU, do inglês Intersection Over Union) e área sob a curva (AUC, do inglês area under the curve). Estas métricas de desempenho são consideradas padrão na literatura para avaliac ¸ão de modelos de detecc ¸ão e segmentac ¸ão [27], [28]. Vale salientar que no cálculo da AUC é feita na máscara não-binarizada, ou seja sem a aplicac ¸ão do limiar.…”

Forecasting Worldwide Temperature from Amazon Rainforest Deforestation Using a Long-Short Term Memory Model

“…The work by the authors of reference [16] highlights a critical international concern: deforestation in the Amazon rainforest. This process has several environmental effects, with the increase in CO 2 emissions and global temperatures being among the most significant issues.…”