AFTERMAP: A FEMA-Aligned Dataset for Damaged Structure Segmentation in UAV Imagery

FloodLens: A Semi-Supervised Segmentation Framework for Post-Flood Assessment using UAV Imagery

One of the worst natural disasters, floods seriously harm infrastructure, economy, and society. The manual interpretation of satellite or aerial imagery is a major component of traditional post-disaster assessment methods, but is time-consuming, labor-intensive, and prone to human error. Automated and precise flood mapping systems that facilitate quick emergency response is becoming more necessary as deep-learning advances and high-resolution UAV datasets become available. This paper focuses on semantic segmentation of flood-affected regions from aerial images by developing a hybrid light weighted and semi-supervised deep learning framework. In this research, from floodNet dataset both unlabeled and labeled UAV imagery, with 10 semantic classes is used and a multi-class segmentation using UNet++ model with ResNet34 and EfficientNet-B4 as backbone encoders is performed. The model is initially trained using annotated images in a supervised learning manner and later is enhanced using a semi-supervised learning approach of pseudo labeling. This training approach is applied to leverage more than 1000 unlabeled images, improving the generalization gradually. With a mean Dice score of 0.84 and mean IoU score of 0.77 for the validation set, EfficientNetB4 performs better while ResNet34 is faster by 3ms of inference time. In effect, the results show that preprocessing and segmentation using proposed models improve further processing of the results in disaster management systems. In addition, pseudo-labeling not only improves performance of minority classes but also offers a scalable method, to manage unlabelled data in substantial amounts, in real-world deployments. Overall, this work testifies the potential of combining the robust encoder-decoder architectures, with semi-supervised learning approach in order to deliver a reliable and efficient preprocessing for automated flood assessment technique. It also provides insights by highlighting the potential for extending the model for preprocessing in real-world applications, such as damage quantification, risk prioritization, etc.

Rapid post-earthquake damage assessment using patch-level CNNs and VLM

This study explores AI-driven image classification to expedite damage evaluation by identifying damaged buildings from post-disaster photos much faster than conventional methods, providing a more detailed understanding of structural integrity across affected areas.

Framework for Cross-Disaster Building Damage Assessment Using Cost-Sensitive Learning

Rapid and reliable assessment of structural damage following disasters is critical for prioritizing rescue operations. In this study, we present a unified deep learning framework for building damage assessment from satellite imagery. The proposed approach integrates segmentation-driven feature construction, morphological processing, cost-sensitive learning, and cross-disaster evaluation to support robust performance under limited and imbalanced data conditions. The framework combines an adapted U-Net for building localization with a hybrid convolutional neural network (CNN)-deep neural network (DNN) classifier for damage-level prediction and evaluates transferability across disaster events, geographic regions, and sensing conditions. The proposed method is evaluated on selected events from the xView2 Building Damage Assessment (xBD) and BRIGHT datasets, using optical imagery from xBD and pre-disaster optical and post-disaster Synthetic Aperture Radar (SAR) imagery from BRIGHT. Despite the limited and highly imbalanced event-specific samples, the framework achieves a mean cross-validation macro-F1 score of 70% and a maximum fold-level score of 77% on the Mexico earthquake subset of xBD and up to 98% on earthquake-related events in BRIGHT. Cross-validation characterizes performance variability across source-image-grouped data partitions, while cross-disaster evaluation reveals event-dependent transferability and provides a preliminary indication that structural domain similarity may be related to transfer performance. Although the evaluation is constrained by data availability, the results indicate that lightweight, cost-sensitive deep learning frameworks may support auxiliary post-disaster screening and decision support in resource-constrained scenarios. This study highlights both the potential and the remaining challenges of deploying artificial intelligence (AI) for rapid post-disaster assessment.

Comparison of U-Net and Attention U-Net for Binary Flood Segmentation from UAV Imagery

Flood disasters consistently cause massive damage every year, making rapid mapping of affected areas crucial for coordinating emergency aid. The use of unmanned aerial vehicles (UAVs) offers a practical solution to obtain high-resolution aerial imagery, but manually identifying flood areas from hundreds of images remains time-consuming. This study analyzes and compares two deep learning segmentation architectures, U-Net and Attention U-Net, for automatic flood area detection from UAV RGB images. Both models were trained using 290 image-mask pairs from a public dataset, with a split of 70% for training, 10% for validation, and 20% for testing. Images were processed at a resolution of 256×256 pixels, normalized to the range [0,1], and augmented with horizontal flipping, brightness adjustment, and affine transformations. Attention U-Net enhances the standard U-Net structure by adding attention gates to all skip connections in the decoder to suppress irrelevant background features. Both models were evaluated across five independent training runs using different random seeds to assess result robustness. Across these runs, Attention U-Net achieved a marginally higher mean IoU (77.11% ± 0.76) and Dice/F1 (87.07% ± 0.49) compared to the U-Net baseline (IoU: 76.97% ± 0.54; Dice/F1: 86.98% ± 0.34), but a paired t-test revealed that these differences were not statistically significant (IoU: p = 0.77; Dice/F1: p = 0.77). These results suggest that, on this dataset, attention gates do not provide a measurable advantage over the standard U-Net architecture, establishing both as comparable practical baselines for future flood mapping research.

Rapid Post-earthquake Damage Detection Using UAV Imagery and YOLO: A Case Study from the Kahramanmaras Earthquake

Multi-Model Evaluation of Semantic Segmentation Techniques for Building Footprint Extraction

In the present generation of increasing geospatial data, accurate and automated extraction of building footprints from high-resolution aerial and satellite imagery has become crucial for various applications such as urban planning, infrastructure development, disaster management, and GIS database maintenance, as manual tracing is time-consuming and unstable for large-scale mapping. This study compares conventional image processing techniques such as thresholding, edge detection, morphological operations through a machine learning approach using Random Forest (RF), and deep learning-based semantic segmentation models, namely U-Net and DeepLabV3+, along with the Segment Anything Model (SAM) using a pre-trained prompt-based setup. All methods are tested on the same set of data, and a standardized data preprocessing is performed for fair comparison. The overall results indicate that the application of DeepLabV3+ is best, with an IoU of 82% and an F1 score of 90%. U-Net achieves second high IoU and F1 scores of 74% and 84% respectively, while Random Forest shows a high IoU of 60% and an F1-score of 72%. SAM has the lowest scores with an IoU of 50% and an F1 score of 51%.