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Open access Sep 2026

Automated detection of bridge and road damage in orthophotos using deep learning

This study proposes a binary deep learning framework to determine whether the bridges or roads in remote sensing data have been damaged, and provides an automated method for image analysis based on remote sensing data.

Xianfeng Li, Jie-An Liang, Shi-Tao Zheng et al. · 0 citations
Open access 2026

DIBNet: Discrepancy-Invariant Boundary-Guided Network for Remote Sensing Change Detection

Remote sensing change detection (RSCD) aims to conduct difference analysis on RS images obtained in different time phases of the same area. It plays a critical role in applications, such as disaster monitoring and forest cover analysis, and has evolved rapidly in recent years. However, how to suppress false changes while enhancing the response to minor real changes and maintaining fine boundaries under the interference of complex backgrounds and imaging differences remains a key challenge in high-resolution RSCD. To address this, this article proposes the Discrepancy-Invariant Boundary-Guided Network (DIBNet). Specifically, to address pseudochanges caused by imaging condition differences, this article proposes a discrepancy-invariant recalibration module, which explicitly utilizes invariant features between different time phases to calibrate the difference features and enhance the ability to suppress false changes. Second, a cross-granularity boundary modeling module is proposed, which uses the details in shallow difference features to provide precise boundary positioning, and introduces semantic context in deep difference features to calibrate the boundary response at the regional level. Through the mutual guidance between semantic granularity and detail granularity, clear and semantically consistent boundary features are generated. Subsequently, a boundary-guided image fusion module is constructed, which injects boundary features into the multiscale difference feature fusion process to improve the response completeness and boundary precision of tiny change regions. Experimental results on multiple public datasets show that the performance of DIBNet is superior to existing mainstream methods.

Kai-Sheng Li, Yan-Yan Mao, Zhao-Long Gao · 0 citations
Open access 2026

SwinMSNet: A Swin Transformer-Based Multiscale Network for Spatio-Environmental Changes

Reliable change detection in remote sensing imagery is critical for supporting applications, such as rapid disaster response and urban development monitoring, where small-scale and incomplete changes are often overlooked. This study proposes SwinMSNet, a Swin transformer-based multiscale network designed to improve recall and robustness in spatio-environmental change detection. The architecture incorporates a cross-scale feature fusion module to capture both large- and small-scale variations, and a contextual feature enhancement block to suppress background noise in high-resolution imagery. To address the problem of incomplete ground truth, a false-positive-guided refinement strategy is introduced to improve annotation quality and enhance the detection of missed changes. Experiments on building and landslide datasets demonstrate that SwinMSNet achieves consistent improvements, with recall enhanced by 17.9% and F1-score by 12.7% over baseline models. These results highlight the potential of SwinMSNet to support real-world Earth observation tasks, including disaster damage assessment, infrastructure monitoring, and land-use planning.

Ching-He Hsu, Pei-Jun Lee, Tsu-En Tien et al. · 0 citations
Open access Jul 2026

PICANTEO: A Modular Change Detection Framework for Remote Sensing Applications

Abstract. This paper presents PICANTEO, a modular and multi-modal change detection framework designed for remote sensing applications in natural disaster response. The framework aims to support damage assessment during both the rapid mapping phase, which occurs in the immediate aftermath of a disaster, and the longer recovery phase. PICANTEO provides automated, reliable disaster-related change detection maps and associated impacted areas to support a wide range of disaster monitoring activities. The integration of uncertainty and ambiguity concepts ensures reliable and qualified results. PICANTEO handles multi-modal remote sensing data, including very high-resolution optical imagery, Digital Surface Models, and Synthetic Aperture Radar (SAR) data. Its modular architecture enables users to apply ready-to-use pipelines or implement their own workflows. The provided scalable components can be combined or extended by custom methods to define new applied pipelines. Several real-world case studies demonstrate PICANTEO’s ability to address various disaster scenarios across diverse geographic contexts. Source code is available at: https://github.com/CNES/picanteo.

Dimitri Lallement, C. Hümmer, D. Youssefi et al. · 0 citations
Open access Aug 2026

Eliminating Temporal Misalignment in SAR Flood Detection with a ConvLSTM-Siamese Approach Using Sentinel-1 Time Series

Abstract. Flood risk has been increasing worldwide due to climate change and rapid urbanization. Rapid and accurate flood mapping is essential for reducing damage and supporting rescue activities. Synthetic Aperture Radar (SAR) has been widely used for flood monitoring because it can observe the Earth’s surface regardless of weather conditions or time of day. Conventional flood detection methods based on change detection between pre- and post-flood SAR images, however, often suffer from false detections caused by seasonal vegetation changes and speckle noise. This study proposes a flood detection method that generates a predicted SAR image representing the normal ground condition using a ConvLSTM model and compares it with an observed SAR image acquired during flooding. To preserve structural information while suppressing noise, the prediction model was trained using the Structural Similarity Index Measure (SSIM) as the loss function. In addition, a Siamese model was employed to model the correlation between predicted and observed images for flood change detection. Experimental results demonstrated that the proposed method reduced false detections and improved overall flood detection accuracy compared with conventional approaches. The results indicate that reducing the temporal gap between comparison images is effective for improving flood detection performance in SAR imagery.

Tatsuya Nakajima, N. Tsutsumida · 0 citations

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