Heavy rains usually hit Laos countrywide and cause serious floods, influencing local agriculture, households, and the economy. Therefore, it is crucial to monitor the flooding in Laos to better understand the flood patterns and characteristics. This paper aims to analyze the influence of the flooding in Laos with multi-source data, e.g., Synthetic Aperture Radar (SAR), optical multi-spectral images, and geographic information system data. First, the flood areas in Laos from 2018 to 2022 are detected using a decision fusion method. Based on the flood areas and the global Land Use/Land Cover (LULC) product, the macro scale global impact of the flood is analyzed. Second, taking the Vientiane Capital as a case study area, a flood forecasting method is applied to estimate the risk of flooding. Finally, optical images before and after the flood event are extracted for a close-up comparison at the micro scale. Based on the above multi-scale analysis, floods in Laos are found to be predominantly concentrated in the flat areas near the Mekong River, with a decreasing trend over time, which could be helpful for flood management and mitigation strategies in Laos. The validation results exhibited notable average indices across a five-year period, with mIoU (0.7782), F1 score (0.7255), and overall accuracy (0.9854), respectively.

Natural disasters pose a threat to the safety of human life and buildings. Rapid and accurate building damage assessment (BDA) on remote sensing images is crucial for disaster response and recovery. However, most methods are constructed on the ideal co-registered bitemporal remote sensing images, neglecting the misalignment in practice. In this paper, we propose a novel building damage assessment method, termed FlowMamba, which can effectively handle the offset between the pre- and post-disaster images in BDA task. Specifically, a vision mamba backbone with four stages is utilized to extract multi scale features from the pre- and post-disaster images. Then, a differential optical flow alignment module is designed to estimate shift matrix to align pre- and post-disaster features. Furthermore, a category distance-aware loss function is tailored for the BDA task, which replaces fixed binary values of the penalty factors in to soft values of inter-class distance. Extensive experiments on the xBD dataset, the BRIGHT dataset and four out-of-distribution disaster scenarios validate the robustness and effectiveness of the proposed FlowMamba. Our code is available at https://github.com/flying318/FlowMamba.

This article presents the scientific results of the 2025 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee, the University of Tokyo, RIKEN, and ETH Zurich. The focus of the contest was to develop innovative solutions for all-weather land-cover and building damage mapping using multimodal SAR and optical EO data at submeter resolution. The contest is organized into two distinct tracks. Track 1 focuses on land-cover mapping and Track 2 is about building damage mapping. The competition presented two primary technical challenges: the effective integration of multimodal data and the development of robust models capable of handling noisy labels. The contest saw significant global engagement, with Track 1 receiving 507 registrations and 3,859 successful submissions, while Track 2 followed a similar trend with 423 registered teams and 5,008 successful entries. This paper provides the methodologies and results achieved by the first- and secondranked teams from each track. To promote transparency and ensure the reproducibility of results, all participating teams in this year's contest have publicly released their code. Furthermore, the dataset used in this competition has been made publicly available to the community to support and encourage further research in the field.

Semantic segmentation of SAR images has garnered significant attention in remote sensing due to the immunity of SAR sensors to cloudy weather and light conditions. Nevertheless, SAR imagery lacks detailed information and is plagued by significant speckle noise, rendering the annotation or segmentation of SAR images a formidable task. Recent efforts have resorted to annotating paired optical-SAR images to generate pseudo-labels through the utilization of an optical image segmentation network. However, these pseudo-labels are laden with noise, leading to suboptimal performance in SAR image segmentation. In this study, we introduce a more precise method for generating pseudo-labels by incorporating semi-supervised learning alongside a novel image resolution alignment augmentation. Furthermore, we introduce a symmetric cross-entropy loss to mitigate the impact of noisy pseudo-labels. Additionally, a bag of training and testing tricks is utilized to generate better land-cover mapping results. Our experiments on the GRSS data fusion contest indicate the effectiveness of the proposed method, which achieves first place. The code is available at https://github.com/StuLiu/DFC2025Track1.git.