Deforestation detection using deep learning-based semantic segmentation techniques: a systematic review

Abstract

Deforestation poses a critical global threat to Earth’s ecosystem and biodiversity, necessitating effective monitoring and mitigation strategies. The integration of deep learning with remote sensing offers a promising solution for precise deforestation segmentation and detection. This paper provides a comprehensive review of deep learning methodologies applied to deforestation analysis through satellite imagery. In the face of deforestation’s ecological repercussions, the need for advanced monitoring and surveillance tools becomes evident. Remote sensing, with its capacity to capture extensive spatial data, combined with deep learning’s prowess in recognizing complex patterns to enable precise deforestation assessment. Integration of these technologies through state-of-the-art models, including U-Net, DeepLab V3, ResNet, SegNet, and FCN, has enhanced the accuracy and efficiency in detecting deforestation patterns. The review underscores the pivotal role of satellite imagery in capturing spatial information and highlights the strengths of various deep learning architectures in deforestation analysis. Multiscale feature learning and fusion emerge as critical strategies enabling deep networks to comprehend contextual nuances across various scales. Additionally, attention mechanisms combat overfitting, while group and shuffle convolutions further enhance accuracy by reducing dominant filters’ contribution. These strategies collectively fortify the robustness of deep learning models in deforestation analysis. The integration of deep learning techniques into remote sensing applications serves as an excellent tool for deforestation identification and monitoring. The synergy between these fields, exemplified by the reviewed models, presents hope for preserving invaluable forests. As technology advances, insights from this review will drive the development of more accurate, efficient, and accessible deforestation detection methods, contributing to the sustainable management of the planet’s vital resources.