The geographical feature extraction of historical maps is an important foundation for realizing the transition from human map reading to machine map reading. The current methods for building block extraction from historical maps have many problems, such as low accuracy and poor scalability. Moreover, the high cost of annotating historical maps further limits its applications. In this study, a method for extracting building blocks from historical maps is proposed based on the deep object attention network. Based on the OCRNet framework, multiple attention mechanisms were used to improve the ability of the network to extract the contextual information of the target. Moreover, through the optimization of the feature extraction network structure, the impact of the down-sampling process on local information and boundary contours was reduced, in order to improve the network’s ability to capture boundary information. Subsequently, the transfer learning method was used to jointly train the network model on both remote sensing datasets and few-shot historical map datasets to further improve the feature learning ability of the network, which overcomes the constraints of small sample sizes. The experimental results show that the proposed method can effectively improve the extraction accuracy of building blocks from historical maps.

A high-resolution sea surface wind field (SSWF) has high application requirements, such as weather forecast, wind energy evaluation, and oil spill monitoring. The models for retrieving SSWFs based on spaceborne synthetic aperture radar (SAR) are important methods for obtaining high-resolution SSWFs. These models are continuously updated and improved from the prototype to the model to achieve high-resolution and high-precision SSWF retrieval. With the development of SAR technology and the gradual maturation of global ocean observations, SSWF quantitative estimation using SAR has developed from scientific research to operational monitoring. This study summarises the principles and methods of SSWF with multipolarized SAR. Finally, research suggestions and future development directions are put forward.