RepDDNet: a fast and accurate deforestation detection model with high-resolution remote sensing image

Forest is the largest carbon reservoir and carbon absorber on earth. Thus, mapping forest cover change accurately is of great significance to achieving the global carbon neutrality goal. Accurate forest change information could be acquired by deep learning methods using high-resolution remote sensing images. However, deforestation detection based on deep learning on a large-scale region with high-resolution images required huge computational resources. Therefore, there was an urgent need for a fast and accurate deforestation detection model. In this study, we proposed an interesting but effective re-parameterization deforestation detection model, named RepDDNet. Unlike other existing models designed for deforestation detection, the main feature of RepDDNet was its decoupling feature, which means that it allowed the multi-branch structure in the training stages to be converted into a plain structure in the inference stage, thus the computation efficiency can be significantly improved in the inference stage while maintaining the accuracy unchanged. A large-scale experiment was carried out in Ankang city with 2-meter high-resolution remote sensing images (the total area of it was over 20,000 square kilometers), and the result indicated that the model computation efficiency could be improved by nearly 30% compared with the model without re-parameterization. Additionally, compared with other lightweight models, RepDDNet also displayed a trade-off between accuracy and computation efficiency.