Wildfires cause substantial damage and present considerable risks to both natural ecosystem and human societies. A precise and prompt evaluation of wildfire-induced damage is crucial for effective post-fire management and restoration. Considerable advancements have been made in monitoring and mapping fire-affected areas through feature engineering and machine learning techniques. However, existing methods often exhibit several limitations, such as complicated and time-intensive procedures on manual labeling, and a primary focus on binary classification, which only distinguishes between burned and non-burned areas. In response, this study develops a wildfire burn severity assessment model, Bi-RAUnet-NBR, which can not only accurately identify fire-affected areas, but also assess the burn severity levels (low, moderate, and high) within those areas. Built upon the standard U-Net architecture, the proposed Bi-RAUnet-NBR first incorporates bi-temporal Sentinel 2 remote sensing imagery, captured before and after a wildfire, which enables the model to better distinguish burned areas from the background and identify the severity level of the resulting burns. Additionally, it further enhances the standard U-Net architecture by fusing additional spectral layers, such as the Normalized Burn Ratio (NBR) derived from post- and pre-fire images, therefore informing the detection of burn areas. Moreover, Bi-RAUnet-NBR also integrates attention mechanism, enabling the model to pay more attention to meaningful features and burn areas, and residual blocks in the decoder module, which not only significantly improves segmentation results but also enhances training stability and prevents the issue of vanishing gradients. The experimental results demonstrate the superiority of the proposed model in both multi-class and binary mapping of wildfire burn areas, achieving an overall accuracy (OA) over 95%. Furthermore, it outperforms baseline algorithms, including support vector machine (SVM), random forest (RF), eXtreme gradient boosting (XGBoost), and fully convolutional network (FCN), with an average improvement of 18% in F1-score and 15% in mean intersection over union (mIoU).

Building change detection and building damage assessment are two essential tasks in post-disaster analysis. Building change detection focuses on identifying changed building areas between bi-temporal images, while building damage assessment involves segmenting all buildings and classifying their damage severity. These tasks play a critical role in disaster response and urban development monitoring. Although supervised learning has significantly advanced building change detection and damage assessment, its reliance on large labeled datasets remains a major limitation. In contrast, self-supervised learning enables the extraction of meaningful data representations without explicit training labels. To address this challenge, we propose a self-supervised learning approach that unifies denoising autoencoders and contrastive learning, enabling effective data representation for building change detection and damage assessment. The proposed architecture integrates a dual denoising autoencoder with a Vision Transformer backbone and contrastive learning strategy, complemented by a Feature Pyramid Network-ResNet dual decoder and an Edge Guidance Module. This design enhances multi-scale feature extraction and enables edge-aware segmentation for accurate predictions. Extensive experiments were conducted on five public datasets, including xBD, LEVIR, LEVIR+, SYSU, and WHU, to evaluate the performance and generalization capabilities of the model. The results demonstrate that the proposed Denoising AutoEncoder-enhanced Dual-Fusion Network (DAEDFN) approach achieves competitive performance compared with fully supervised methods. On the xBD dataset, the largest dataset for building damage assessment, our proposed method achieves an F1 score of 0.892 for building segmentation, outperforming state-of-the-art methods. For building damage severity classification, the model achieves an F1 score of 0.632. On the building change detection datasets, the proposed method achieves F1 scores of 0.837 (LEVIR), 0.817 (LEVIR+), 0.768 (SYSU), and 0.876 (WHU), demonstrating model generalization across diverse scenarios. Despite these promising results, challenges remain in complex urban environments, small-scale changes, and fine-grained boundary detection. These findings highlight the potential of self-supervised learning in building change detection and damage assessment tasks.

Building damage assessment is a critical subtask within GeoAI-driven remote sensing semantic segmentation, where deep neural networks have been widely applied. Most existing works typically use pre- and post-disaster images as input of a siamese deep neural network under supervised learning, which requires a large amount of labeled data. However, in real-world scenarios, acquiring massive labeled datasets is often difficult, making fully supervised methods less practical. To overcome this, we propose a self-supervised pretraining framework based on pre-post remote sensing image pairs. In the first stage, a dual denoising autoencoder with Vision Transformer backbone is proposed for image representation learning. In the second stage, two downstream tasks-building localization and building damage severity-are performed. Additionally, we incorporate an edge guidance module and an edge detection loss to further enhance performance in downstream tasks. On the xBD dataset, the largest building damage assessment dataset, the proposed method achieves an F1 score of 0.895 for building localization, outperforming state-of-the-art image segmentation techniques. Additionally, it receives an F1 score of 0.704 in building damage severity compared to state-of-the-art in all self-supervised learning methods.