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.

Multispectral (MS) and hyperspectral (HS) imageries are widely used in agriculture, environmental monitoring, and urban planning due to their complementary spatial and spectral characteristics. A fundamental tradeoff persists: MS imagery offers high spatial but limited spectral resolution, while HS imagery provides rich spectra at lower spatial resolution. Prior HS generation approaches often struggle to jointly preserve spatial detail and spectral fidelity. In response, we propose SpecSwin3D, a Swin Transformer-based model that generates HS imagery from MS inputs while preserving both spatial and spectral qualities. Specifically, SpecSwin3D uses five MS input bands to generate 224 HS bands at the same spatial resolution. In addition, existing methods that construct all HS bands using a single global model suffer from increasing generation errors for bands that are spectrally distant from the input bands, while training separate models (band-specific) for individual bands is computationally intensive. To address this tradeoff, we propose a curriculum-based cascade training strategy that progressively expands the spectral range from easier, MS-adjacent bands to more challenging, spectrally distant bands. This approach enables stable learning from spectrally proximal to distal bands and improves reconstruction fidelity for each individual band while significantly improving computational efficiency. Moreover, we design an optimized band sequence that strategically repeats and orders the five selected MS bands to better capture pairwise relations of the spectrum within a 3-D shifted-window Transformer framework. Quantitatively, our model achieves a peak signal-to-noise ratio (PSNR) of 35.84 dB, spectral angle mapper (SAM) of 2.39°, and structural similarity index metric (SSIM) of 0.96, outperforming state-of-the-art deep learning based approach by +5.8 dB in PSNR and reducing ERGAS by more than half. Beyond HS band generation, we further demonstrate the practical value of SpecSwin3D on two downstream tasks, including land-use classification and burned area segmentation, achieving satisfactory results with enhanced both spatial and spectral resolutions. Although SpecSwin3D uses a single unified model, cascade training substantially reduces the training budget for the majority of target bands (e.g., from 80 to 20 epochs at later levels), resulting in about 75% lower training cost compared with uniform training.