Satellite Image-Based Multi-Hazard Deep Learning Framework for Near-Real-Time Flood, Wildfire, and Landslide Classification Using Vision Transformers
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Abstract
The increasing occurrence and severity of natural disasters have created a growing demand for automated systems capable of analyzing large geographical regions efficiently, consistently, and with minimal manual intervention. Floods, wildfires, and landslides can cause substantial damage to infrastructure, agriculture, ecosystems, and human settlements, while their spatial distribution and visual characteristics may vary considerably across geographical locations and environmental conditions. Satellite remote sensing provides an effective means of observing large and potentially inaccessible areas and offers valuable information for disaster assessment, environmental monitoring, and emergency-response activities. However, many existing disaster-analysis approaches are developed for individual hazard categories and frequently rely on convolutional neural networks (CNNs), whose feature extraction mechanisms primarily emphasize local spatial relationships. Such representations may be insufficient when the interpretation of a disaster scene depends on contextual information distributed across distant regions of a satellite image.
To address these challenges, this study proposes a Multi-Hazard Vision Transformer (MH-ViT) framework for unified classification of satellite imagery into four categories: flood, wildfire, landslide, and normal environmental conditions. The proposed framework employs a ViT-B/16 backbone together with multi-level transformer representations, multi-scale feature extraction, hazard-aware attention, feature fusion, and a dedicated confidence-estimation head. The multi-level transformer representations capture complementary visual information from different depths of the encoder, while multi-scale feature extraction is designed to preserve both fine-grained local patterns and broader regional and global contextual information. A Hazard-Aware Attention module further emphasizes informative feature channels and spatial regions associated with potential hazard signatures while reducing the influence of less relevant environmental content. The resulting representations are integrated through a feature-fusion mechanism before being passed to the classification head. In parallel, the dedicated confidence-estimation head provides an additional measure of prediction reliability and supports the identification of uncertain observations that may require subsequent human inspection.
The experimental benchmark contains 10,000 high-resolution satellite images, with 2,500 samples assigned to each of the four classes. The dataset incorporates geographical and environmental diversity, including variations in cloud coverage, terrain shadows, atmospheric haze, sensor noise, vegetation conditions, and hazard scale. MH-ViT is evaluated against ResNet-50, ViT-B/16, and Swin Transformer under a common dataset partition, preprocessing pipeline, training configuration, and held-out evaluation protocol. The experimental framework includes overall and class-wise performance evaluation using accuracy, precision, recall, macro-averaged F1-score, and ROC-AUC, together with confusion-matrix analysis. In addition, ablation experiments are conducted to examine the contribution of multi-scale feature extraction, hazard-aware attention, and feature fusion. Confidence and calibration analysis, attention-based interpretability analysis, and computational benchmarking are also incorporated to assess prediction reliability, model behavior, and inference efficiency. The final quantitative results are obtained directly from the reproducible experimental implementation so that the reported performance remains consistent with the proposed methodology and evaluation procedure
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