Remote Sensing

Rapid urbanization in coastal regions presents complex challenges for environmental management and public safety. Accurate, high-resolution wind field monitoring is critical for urban disaster mitigation, infrastructure resilience, and pollutant dispersion analysis in these densely populated areas. However, utilizing massive multi-source satellite remote sensing data for precise prediction remains difficult due to the spatiotemporal heterogeneity caused by the land–sea interface. To address this, this study proposes a novel lightweight Geospatial Artificial Intelligence (GeoAI) framework (DA-DSC-UNet) designed to predict wind fields in coastal urban environments (e.g., Fujian, China). We constructed a dataset by integrating multi-source satellite scatterometer products (including Advanced Scatterometer (ASCAT), Fengyun-3E (FY-3E), and Quick Scatterometer (QuickSCAT)) and buoy observations. The framework employs a UNet architecture enhanced with dual attention mechanisms (Efficient Channel Attention (ECA) and Convolutional Block Attention Module (CBAM)) to adaptively extract features from remote sensing signals, focusing on critical spatial regions like urban coastlines. Additionally, depthwise separable convolutions (DSCs) are introduced to ensure the model is lightweight and efficient for potential deployment in urban monitoring systems. Results demonstrate that our approach significantly outperforms existing deep learning models (reducing Mean Absolute Error (MAE) by 14–25.8%) and exhibits exceptional robustness against observational noise. This work demonstrates the potential of deep learning in enhancing the value of remote sensing data for urban resilience, sustainable development (SDG 11), and environmental monitoring in complex coastal zones.

Machine Learning (ML) modeling for disaster management is a growing field, but existing works focus more on mapping the extent of floods or broad categories of damage and they lack methods for explainability to help users understand model outputs. In this study, we propose Flood Assessment using Dempster–Shafer Fusion (FADS-Fusion), a tool for addressing post-flood damage assessment using Dempster–Shafer fusion to combine outputs from multiple deep learning models. FADS-Fusion is generalized to use any pretrained models, once outputs are post-processed for consistency, making it applicable for other disaster management or change detection applications. The novelty of our work comes from the application of Dempster–Shafer for multi-model fusion and uncertainty quantification on a flood dataset for segmenting both buildings and roads. We trained and evaluated models using the SpaceNet 8 challenge dataset and demonstrated that the fusion of the SpaceNet 8 Baseline (SN8) and Siamese Nested UNet (SNUNet) models has a modest overall improvement +1.93% to mAP, while a +12.3% increase for Precision and a −15.0% decrease in Recall are statistically significant compared to the baseline. FADS-Fusion also quantifies uncertainty by using the conflict of evidence, with a discount factor, with Dempster–Shafer fusion as both a quantitative and qualitative explainability method. While uncertainty correlates with a drop in performance, this relationship depends on values for class-weighted uncertainty and location. Mapping uncertainty back onto the original image allows for a visual inspection on fusion quality and indicates areas where a human will need to reassess. Our work demonstrates that FADS-Fusion improves post-flood segmentation performance and adds the benefit of uncertainty quantification for explainability, an aspect important for reliability and user decision-making but understudied in ML for disaster management in the literature.

Ku-band scatterometers lose extensive Sea Surface Vector Wind (SSVW) observations under extreme winds, heavy precipitation, or instrument anomalies, degrading forecast and assimilation skill. Traditional interpolation fails to reconstruct non-linear wind structures, whereas existing deep learning inpainting is hampered by scarce public datasets, high computational cost and insufficient continuity modeling. We propose WMamba, an Attention-Structured State Space Duality (ASSD)-based framework that exploits wind continuity to encode global dependencies with O(N) complexity for accurate SSVW inpainting. A Grouped Multiscale Attention Block (GMAB) ensures accurate fine-scale wind detail reconstruction by mitigating local pixel degradation. We also introduce L-WMamba, a lightweight 0.36 M-parameter variant suitable for resource-limited devices. Moreover, we release the SSVW Inpainting Dataset (WID), comprising 123,841 high-wind HY-2B HSCAT samples (2018–2022), as an open benchmark. Experiments demonstrate that WMamba outperforms GRL (state-of-the-art) decreasing the RMSE for wind speed and direction by 11.4% and 6.3%, respectively, while achieving a 94.7% reduction in parameters. In particular, WMamba effectively inpaints wind details, as evidenced by the highest MS-SSIM and RAPSD scores. This framework and dataset establish a robust baseline for extreme-weather SSVW recovery.