Towards Advanced Wildfire Analysis: A Siamese Network-Based Change Detection Approach through Self-Supervised Learning

Escalating wildfire incidents necessitate improved post-disaster management practices for more effective response and recovery. This study advances the integration of Earth Observation technologies into the wildfire damage assessment phase, contributing a novel approach to augment disaster recovery efforts. Multi-temporal satellite imaging is crucial for monitoring wildfire-affected areas, and the widespread availability of multispectral images with high revisit frequencies substantially improves the comprehensive study of these changes. This paper presents an examination of deep learning techniques for change detection, employing a Siamese convolutional neural network enhanced with an Atrous Spatial Pyramid Pooling block for efficient image data processing. The model is trained and validated on the "Sentinel-2 Wildfire Change Detection Dataset" (S2-WCD), a custom-made dataset aimed at change detection methodologies. By introducing this specialized dataset and applying advanced neural network techniques, the study fills crucial research gaps, offering improvements in wildfire disaster management, particularly in the critical recovery phase following wildfire events.