Rapid Building Damage Detection from Remote Sensing Images: a Novel Lightweight Network with Contrastive Learning

Implementation of a Novel Adaptive Deep Learning-based Change Detection Framework under Flood Environment

An important and efficient Remote Sensing (RS) application is change detection, as it aids in locating the crucial changed areas and offers respective time-series data with the assistance of RS imagery. Here, one of the common and unexpected tragedies is flood, which affects the lives of people and the basic needs of the public. Thus, it is essential to tackle several issues that take place in the classical change detection models. In this research, a new learning-based model is introduced for flood change detection. At the beginning, essential validation data are gathered from benchmark deep learning sources. After data collection, images are fed into a developed deep learning framework. The detection of flood-related changes is then carried out through the SCA-MCA-E-ADDUNet[Formula: see text] model, which integrates Spatial Cross Attention and Multi-Convolution Attention Fusion Encoder within an Adaptive-Dilated DenseUNet[Formula: see text] structure. Moreover, various parameters in ADDUNet[Formula: see text] are optimised using the Updated Alpha Value-based Enhanced Wild Gibbon Optimisation Algorithm (UAV-EWGOA), which aids in improving the change detection efficiency. Finally, the change detection outcomes are obtained from SCA-MCA-E-ADDUNet[Formula: see text]. Later, various experiments are executed to verify the overall change detection efficiency of existing models. Here, Dice coefficient of SCA-MCA-E-ADDUNet[Formula: see text]-UAV-EWGOA is 94.81%, Intersection over Union (IoU) is 90.14%, accuracy is 94.9%, specificity is 95.08% and F1-score is 94.81%, respectively. Thus, the result was highly effective in detecting changes, potentially surpassing the capabilities of traditional or classical models in this specific change detection task.

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.

ELFFNet: An Efficient Lightweight Feature Fusion Network for Change Detection

Remote sensing change detection is essential for land use planning, urban monitoring, and disaster response. However, conventional deep learning methods often suffer from blurred building and vegetation edges, shadow misclassification, and low efficiency due to excessive complexity. To address these issues, we propose ELFFNet, a lightweight and efficient network that achieves high detection accuracy with reduced computational cost. ELFFNet integrates Asymmetric Dilated Convolution Modules for deep semantic extraction, Multi Efficient Attention Modules between encoder and decoder to retain fine spatial details, and a simplified Pyramid Pooling Module at the deepest stage for low-cost global context aggregation. Experiments on the GVLM-CD and WHU-CD data sets show superior performance, with Kappa coefficients of 82.86% and 94.48%, F1 scores of 91.72% and 95.24%, and mean intersection over union values of 84.96% and 94.73%. Importantly, ELFFNet requires a parameter size of 7.31 MB and 22.6 giga floating-point operations [GFLOPs])—far fewer than Transformer-based models—while delivering strong edge detection for small targets and complex disaster areas. This balance of accuracy and efficiency makes ELFFNet highly suitable for practical remote sensing applications.

DisasterChangeNet: Boundary-Preserving Deep Change Detection in Remote Sensing for Disaster Management

A Deep Learning Framework for Rapid Building Damage Detection through Multimodal Data Fusion: Application to the 2025 Myanmar Earthquake

Abstract. Rapid and reliable assessment of building damage after major earthquakes is essential for effective emergency response and recovery planning. This study formulates post-disaster building damage detection (BDD) as a binary image classification task (damaged vs. undamaged buildings) using multimodal satellite data and a unified ResNet-18 backbone to enable a controlled comparison of fusion strategies. The analysis focuses on the Mw 7.7 Myanmar earthquake of 28 March 2025 and integrates post-event COSMO-SkyMed Second Generation (CSG) dual-polarization (HH, HV) SAR imagery, Maxar optical data, OpenStreetMap (OSM) building footprints, and UNOSAT damage annotations. Three fusion paradigms are evaluated: Early Fusion (EF), Late Fusion (LF), and a novel Middle Fusion (MF) approach. The proposed MF framework introduces a Footprint-Guided Cross-Attention (FGCA) mechanism that uses building geometry as a spatial prior to guide feature-level interaction between SAR and optical representations. Five-fold cross-validation results show that MF consistently outperforms EF and LF, achieving higher precision, F1-score, and robustness across modality configurations. By jointly exploiting SAR structural sensitivity, optical detail, and footprint-based spatial context, the proposed Footprint-Guided Middle Fusion (FGMF) framework enables accurate and scalable building damage mapping from heterogeneous Earth Observation (EO) data.

RSCDG: Remote Sensing Change/Damage Image Generator Based on Prior Foundation Model and Multimodal Reference Information

Abstract. In scenarios such as natural disasters, military conflicts, and rapid urban expansion, high-quality post-event remote sensing images are often difficult to obtain in a timely manner, limiting the training and application of change detection, damage assessment, and related interpretation models. To address this issue, this paper proposes RSCDG, a remote sensing change/damage image generation framework based on prior foundation models and multimodal reference information. Built on a pretrained latent diffusion model, RSCDG integrates three types of conditional information: a Pre-event Visual Prompt Adapter extracts structural priors from the pre-event image via Prithvi-EO-2.0 to preserve background stability in unchanged regions; a Spatial Location Control Pathway introduces the change/damage mask into a ControlNet branch to improve spatial precision; and a Generation Content Text Controller uses a CLIP text encoder to guide semantically consistent generation. In addition, a Mask Alignment Loss is introduced to align the change patterns of generated and real images under the supervision of a frozen change detection model. Experiments on the LEVIR-MCI change scenario and the CEBD earthquake damage scenario show that RSCDG consistently outperforms ControlNet. In the change scenario, it achieves an FID of 28.92, an IS of 9.62, and a KID of 0.0139; in the damage scenario, the corresponding values are 37.82, 8.05, and 0.0187, respectively. Ablation results further confirm the effectiveness of the Mask Alignment Loss. Overall, RSCDG provides a practical solution for post-event sample construction, change detection data augmentation, and controllable sample generation for damage assessment.