Jul 2026· International Conference on Smart Communications and Networking· pp. 1-7· 0 citations· 20 references
Abstract
Automated building damage assessment from postearthquake satellite imagery is a critical yet severely data-limited task. The xView2 Mexico earthquake subset presents an extreme class imbalance: of 6,105 annotated buildings extracted from 121 scene pairs, 99.1% are no-damage, while the destroyed class contains only one instance—a 6,052:1 imbalance ratio that renders conventional classifier training infeasible for minority classes. We propose DiffAugment-xView2, a three-stage framework that combines (1) Low-Rank Adaptation (LoRA) fine-tuning of Stable Diffusion v1.5 on post-disaster building patches, (2) ControlNetguided conditional generation using Canny-edge maps from predisaster imagery to preserve building geometry, and (3) targeted synthetic oversampling of underrepresented damage classes before EfficientNet-B4 classifier training. Compared to a noaugmentation baseline, DiffAugment-xView2 achieves a measured macro-F1 improvement of +9.5 percentage points (0.217 → 0.312) and raises destroyed-class F1 from 0.388 to 0.733 (+88.9% relative)—measured on EfficientNet-B4 with an NVIDIA A100 GPU on a 231-patch held-out test set (131 real class 0–2 patches from an 80 / 20 stratified split, plus 100 ControlNet-generated synthetic destroyed patches withheld from all training, seed = 42). Our real dataset analysis confirms that the severity of class imbalance in disaster-affected regions far exceeds prior reports, making diffusion-based augmentation not merely beneficial but necessary for practical deployment.
Transportation networks are critical for emergency response after earthquakes, but national-scale bridge and viaducts inventories often lack vulnerability-related attributes such as material and structural system. This paper presents an image-based approach, developed within the SAFENET project, to automatically classify bridges/viaducts according to a practical Material-Structure (MS) labeling scheme that reduces sparsity compared to finer taxonomies that also include construction period. Using a Portuguese bridge image dataset, we compare three visual model families for Material-Structure classification: a ResNet-50 convolutional baseline, a self-supervised vision transformer (DINOv2-Large), and a contrastive vision encoder (CLIP). Models are evaluated with a strict 5-by-5 Nested CrossValidation (NCV) protocol with bridge-level splits to prevent information leakage across train and test sets. Results show that DINOv2 achieves the best overall performance, with a mean accuracy of 0.903, a macro-F1 of 0.773, and a weighted-F1 of 0.897, outperforming ResNet-50 and CLIP especially on minority classes. These findings support the use of self-supervised vision transformers to enrich bridge inventories from imagery and to provide scalable inputs for regional seismic risk assessment.
Tomás Oliveira, Rui S. Moreira, Feliz Gouveia et al.· International Conference on...· 0 citations
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.
M. Kovačević, F. Đorđević, Đorđe Nedeljković et al.· Bulletin of Earthquake Engin...· 0 citations
Abstract. In the aftermath of a disaster, whether natural, industrial, or war-related, a rapid and accurate assessment of building damage is crucial for rescue forces to conduct an effective emergency response. Very high-resolution satellite imagery enables such assessments and serves as an important indicator for understanding the scale of destruction, supporting time-critical rescue operations, and guiding resource allocation. While deep learning models have shown promising results in automating building damage assessment (BDA) from pre- and post-disaster optical satellite imagery, they often fail to generalize to new disasters due to domain shifts. This paper studies the challenge of rapid domain adaptation for BDA in the context of the war in Ukraine. We create a new, challenging dataset annotated with damage grades across six cities in Ukraine, using pre- and post-disaster optical imagery. To facilitate rapid adaptation, we propose an efficient fine-tuning workflow using Low-Rank Adaptation. Our experiments show that this approach substantially improves performance in both out-of-domain and in-domain settings, presenting a practical and data-efficient study for deploying BDA models in time-critical emergency scenarios.
Sebastian Gapp, C. Henry, Pablo d'Angelo et al.· ISPRS Annals of the Photogra...· 0 citations
A hybrid framework that decouples detection from damage assessment is proposed, combining the precision of CV models with the reasoning power of LVLMs, and the best combination under this framework accurately counts intact, partially damaged and completely destroyed buildings.
H. Ung, Guillaume Habault, Roberto Legaspi et al.· 0 citations
Natural disasters, particularly wildfires, cause severe human, environmental, and economic losses worldwide. Rapid and accurate identification of building footprints and potential potential structural changes is essential for effective emergency response, search-and-rescue operations, and post-disaster recovery planning. To address the challenges of identifying building loss from remote sensing imagery, this study proposes CalFireSegNet, a lightweight hybrid attention–transformer network for post-wildfire building footprint extraction and loss proxy detection from satellite imagery. The proposed architecture integrates depthwise convolutions, convolutional block attention modules (CBAM), atrous spatial pyramid pooling (ASPP), and Transformer blocks to effectively capture both local structural details and long-range contextual dependencies while maintaining low computational complexity. The model was trained and evaluated using benchmark building segmentation datasets (Inria and WHU) and subsequently applied to pre- and post-event satellite imagery from the recent California wildfires. Experimental results demonstrate that CalFireSegNet achieves superior performance compared with several state-of-the-art semantic segmentation models, including U-Net, PSPNet, DeepLabv3+, ENet, HRNet, and SegNet, obtaining 98.45% accuracy, 94.35% mIoU, and 95.03% Dice Similarity Score while requiring only 3.72 million parameters. Furthermore, a lightweight mask-difference framework was developed to generate a spatial proxy of potential building footprint loss using pre- and post-event satellite pairs. Since publicly available building-level damage annotations for recent California wildfire events remain limited, the real-world wildfire experiments are presented as a validation of cross-domain applicability rather than a fully supervised structural loss proxy estimation benchmark.
Abdullah Şener, Vedat Tümen, B. Ergen et al.· Scientific Reports· 0 citations
The increasing unpredictability of weather patterns due to climate change necessitates robust, real-time flood monitoring systems for high-risk disaster-prone cities. Existing monitoring approaches primarily utilize satellite-based remote sensing, in-situ telemetry such as ultrasonic level detection and precipitation gauges, or convolutional neural networks trained on heterogeneous, crowd-sourced internet imagery. However, the effectiveness of these systems is limited by Severe Inundation Visual Paucity (SIVP), which refers to the scarcity of historical visual data depicting extreme, high-stage inundation events required for training deep learning classifiers. To address this data gap, this study presents Semantically-Guided Hydro-Synthesis (SGHS), a novel generative framework that employs latent diffusion models to synthesize photorealistic, weather-variant inundation overlays on fixed CCTV perspectives. Unlike prior generative flood-data approaches that operate from satellite or aerial perspectives, produce numerical depth grids, or rely on site-agnostic image priors, SGHS is uniquely conditioned on a site-specific UAV-reconstructed 3D digital twin, employs prompt-driven domain randomization across six atmospheric parameters, and is governed by a formalized ten-level anthropometric severity taxonomy that enables graduated, decision-relevant severity estimation rather than binary flood/no-flood inference. By using SGHS to generate a balanced dataset encompassing escalating flood severities and diverse meteorological conditions, the SIVP constraint is directly mitigated, thereby enriching the training distribution with essential high-stage instances absent from historical records. Empirical results are reported across three evaluation tiers with disaggregated test-set provenance and across ten random seeds with mean and standard deviation. On the Tier-1 sim-to-real holdout at Levels 0 through 3, the raw-data baseline achieves Accuracy <inline-formula> <tex-math notation="LaTeX">$=0.1990~\pm ~0.0146$ </tex-math></inline-formula> and F<inline-formula> <tex-math notation="LaTeX">$1=0.2170~\pm ~0.0160$ </tex-math></inline-formula>, while the SGHS-augmented curated classifier achieves Accuracy <inline-formula> <tex-math notation="LaTeX">$=0.7700~\pm ~0.0099$ </tex-math></inline-formula> and F<inline-formula> <tex-math notation="LaTeX">$1=0.7825~\pm ~0.0108$ </tex-math></inline-formula> (Wilcoxon signed-rank, p < 0.002). Matched-augmentation controls applying focal loss, balanced sampling, and standard photometric augmentation to the raw partition reach only F<inline-formula> <tex-math notation="LaTeX">$1=0.3120~\pm ~0.0211$ </tex-math></inline-formula>, isolating the contribution of generative augmentation. Tier-3 results at Levels 6 through 9 (F<inline-formula> <tex-math notation="LaTeX">$1=0.6685$ </tex-math></inline-formula>–0.7135) are explicitly reframed as evidence of internal consistency within the simulation domain rather than as a claim of sim-to-real operational transfer, since extreme-severity ground truth remains structurally absent from the observational record. These results indicate that site-specific, semantically-guided generative augmentation provides a measurable and statistically significant improvement over both raw-data and algorithmic class-imbalance baselines, addressing hydrological data scarcity for vision-based disaster response systems.
Rocelle Andrea S. Belandres, Miguel Antonio M. Sumulong, John Anthony C. Jose· IEEE Access· 0 citations