TL;DR
This work proposes a novel multi-level feature-learning framework that incorporates adaptive image enhancement, hybrid segmentation, multi-texture feature extraction, attention-based classification, and a self-adaptive optimization strategy within a single architecture for image classification of natural disasters.
Natural disasters, particularly wildfires, pose a serious threat to ecosystems, human lives, and infrastructure, making rapid and accurate fire detection essential for effective disaster management. Despite recent advances in deep learning-based wildfire detection, conventional CNN-based approaches are inherently limit...
A unified deep learning framework for building damage assessment from satellite imagery that integrates segmentation-driven feature construction, morphological processing, cost-sensitive learning, and cross-disaster evaluation to support robust performance under limited and imbalanced data conditions is presented.
In this research, a new learning-based model is introduced for flood change detection, which was highly effective in detecting changes, potentially surpassing the capabilities of traditional or classical models in this specific change detection task.
Rapid multihazard disaster recognition is a key technique for automatic interpretation of immediate remote sensing imagery and plays an important role in emergency response, damage assessment, and risk mitigation. Deep convolutional neural networks (CNNs) have shown strong representation ability for disaster classifica...
This paper proposes an enhanced landslide detection method based on the RT-DETR-r18 framework, aiming to improve detection accuracy, efficiency, and robustness and introduces an adaptive deformable attention module, AIFI-DAttention, which combines reference point offsets and multi-head attention mechanisms to strengthe...
Effective post-disaster damage assessment is critical for supporting timely disaster response and recovery efforts. To address the challenges of large-scale damage analysis, in this study, we propose a novel deep learning model, the Dilated LinkNet Hierarchical Radon Transformer (DL-HRT) network. The DL-HRT network int...
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