Abstract
Understanding the dynamics of deforestation and land uses of neighboring areas is of vital importance for the design and development of appropriate forest conservation and management policies. In this article, we approach deforestation as a multilabel classification (MLC) problem in an endeavor to capture the various relevant land uses from satellite images. To this end, we propose a multilabel vision transformer model, ForestViT, which leverages the benefits of the self-attention mechanism, obviating any convolution operations involved in commonly used deep learning models utilized for deforestation detection. Experimental evaluation in open satellite imagery datasets yields promising results in the case of MLC, particularly for imbalanced classes, and indicates ForestViT's superiority compared with well-established convolutional structures (ResNET, VGG, DenseNet, and ModileNet neural networks). This superiority is more evident for minority classes.
Highlights
C OMPREHENDING and monitoring deforestation and its implications may have an impact on climate change through greenhouse gas emissions reduction [7]
We show that the self-attention mechanism achieves competitive or better results compared with wellestablished Convolutional neural networks (CNN) methods in deforestation monitoring, especially, as regards the less frequent classes in the dataset
2) Deep Learning in Deforestation: deep learning (DL) [32] techniques are popular in remote sensing applications [28], [38]; as such, there are already a few studies related to the application of DL methods to the deforestation detection problem [25]
Summary
C OMPREHENDING and monitoring deforestation and its implications may have an impact on climate change through greenhouse gas emissions reduction [7]. Self-attention enhances nonlocal relationships across the image and learns complex connections between neighbors and to the neighbors’ neighbors (long-range dependencies), which can be beneficial for our MLC problem. We show that the self-attention mechanism achieves competitive or better results compared with wellestablished CNNs methods in deforestation monitoring, especially, as regards the less frequent classes in the dataset. Design, and train a vision transformer model to identify the driving forces of deforestation (agriculture, habitation, infrastructures, and other drivers) of primary forest loss using satellite imagery in the Amazon rainforest. We propose a transformer network architecture for an MLC problem in satellite imagery data, which has the comparative advantage in modeling long-range spatial dependencies and is capable of handling imbalanced datasets
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