Personnel and boat detection in unmanned aerial vehicles (UAVs) imagery plays a crucial role in open water search and rescue missions. The diverse perspectives and altitudes of UAV images often result in significant variations in the imagery’s appearance and dimensions of personnel and boats, and the false detections arising from water surface flares are acknowledged as a great challenge as well. The existing deep learning-based detection methods employ convolutional blocks with fixed kernel sizes to extract features from the imagery at a fixed spatial scale, which will lead to missed and false detections and severely affect detection accuracy when there are substantial differences in the appearance and size of the target objects. In this article, a spatial scale adaptive real-time object detection neural network, namely YoloOW, was proposed to tackle the challenge of personnel and boat detection amid the diverse UAV imagery, which comprises a feature extractor, a feature enhancer, and a postprocessor. The OaohRep convolutional block was proposed as a pivotal component in constructing the YoloOW and applied to the feature extractor and the feature enhancer. Compared with general convolution blocks, the OaohRep convolution block can extract image features across a wide range of spatial scales, show better scale adaptability, and achieve faster detection speed due to its unique merged convolution layer design. OaohRepBi-path aggregation network (PAN) was proposed in the feature enhancer, which imitated the architecture of the classic algorithm scale invariant feature transform (SIFT) and was successfully applied to deep learning models, showing better scale adaptability. A novel UAV detection box filter (UDBF) module was proposed in the postprocessor, which can effectively remove false detections caused by water surface flares. Experimental results demonstrate that our YoloOW model achieves 37.18% mean averaged precision (mAP) on the SeaDronesSee dataset, surpassing the baseline by 8.43%. This notable improvement positions our model at the first of the leaderboard. The code will be available at https://github.com/Xjh-UCAS/YoloOW .

Flood segmentation using synthetic aperture radar (SAR) images is essential for determining the extent of inundation areas, and informing subsequent management recommendations. However, existing networks for flood segmentation using single modality SAR images often face inherent challenges, including interference from terrain shadows and water-like surfaces, leading to degraded segmentation performance. In this study, we introduced a multi-level multi-modal fusion network (FloodNet), in which an Adaptive Gated Feature Fusion Module (AGFFM) is designed to integrate multi-modal features from Sentinel-1 SAR images, Digital Elevation Model (DEM) and Joint Research Centre Global Surface Water (JRC-gsw). Furthermore, we proposed a semantic consistency constraint strategy to alleviate the blurring of water edges during the prediction process. Experiments on two publicly available flood datasets, C2S-Flood and ETCI-Flood, demonstrate the competitive performance of the proposed FloodNet compared with other state-of-the-art single- and multi-modal networks. The code is available at https://github.com/SuperPixelPioneer/Flood-Net.

Efficient extraction of building footprints from aerial and satellite imagery is essential for urban planning, infrastructure management, and large-scale geospatial analysis. Traditional raster-based approaches provide limited geometric precision, while existing polygon-generation methods often rely on detecting and ordering small-scale building vertices, which can lead to incomplete structures, distorted shapes, and high computational cost. To address these limitations, this study proposes an Edge-Attentive Dual-Branch Frame Field Network (EA-DBFFN) for automated and high-precision building polygon extraction. The method is built upon frame field learning and introduces a dual-branch architecture that separately predicts building masks and edges. A Dual-Task Decoder enlarges and adapts receptive fields while applying spatial attention to enhance the representation of structural details. Fixed Sobel and Laplacian filters are incorporated to strengthen boundary detection. In addition, a Dual-Task Mutual Guidance Module promotes the exchange of complementary information between the mask and edge branches, improving geometric consistency and reducing boundary errors. Experiments conducted on the Inria Aerial dataset and the CrowdAI dataset demonstrate that EA-DBFFN achieves superior performance in region-based metrics, with an AP75 of 72.9% on CrowdAI, representing a 2.3% improvement over competing methods. Furthermore, EA-DBFFN produces geometrically higher-quality polygons, with the Max Tangent Angle error reduced by 6.4%, the Invalid Polygon Ratio reduced by 66.3%, and Edge Smoothness improved by 72.7% compared to the best competing method. The results show that EA-DBFFN provides an effective and computationally efficient framework for generating high-quality vectorized building footprints suitable for large-scale urban analysis.