Rapid detection of oil tank targets has become a topic of significant and increasing interest because the quick acquisition of the distribution and volume of oil storage tanks has high economic and military value. However, research in the literature still faces many challenges in different scenarios. This letter presents a new oil tank detection approach for improved EfficientDet model. The proposed approach has three primary operations: 1) first, adding 3-D deformable convolution to the EfficientDet model as predetection to limit detection in the smaller area; 2) second, loading an attention mechanism appropriate for oil tank detection on the myNet model highlights tiny target information; and 3) lastly, training the myNet model repeatedly by focal loss function to seek better results. The result shows that the overall accuracy of mean average precision in the proposed approach increased by at least 8.25% compared with that of the tested conventional approaches. Therefore, it provides advantageous capabilities for monitoring oil tanks in high-resolution remote sensing imagery.

Automatic detection of damaged buildings from satellite remote sensing data has become an urgent problem to rescue planners and military personnel. Unfortunately damaged buildings are in different dimensions and shapes with different roofs depending on the type of the material to be painted. In this study, we present an improved Swin‐Unet approach that comprises three main operations. First, improved Swin‐Unet as a Unet‐like pure Transformer is used for multitemporal image segmentation. Second, different multitemporal features are extracted using hyperspectral image classification algorithm. Finally, a binary change map is generated, and evaluation results are obtained. This article takes AIST building change detection scene as the example, and compared with the conventional approaches tested, overall accuracy, mean intersection over union, and separated Kappa in the proposed method were improved by at least 23.36, 0.1725, and 0.202, respectively. Furthermore, different scenes, such as Gaofen‐2/Jilin‐1 multitemporal optical images and satellite imagery dataset (xBD), have also come to the same conclusion. Thus, it provides advantageous capabilities for monitoring damaged buildings along coastal areas.