• Digital Humanities and Cultural Heritage
• Open Access close
• Chinese close
• Acta Geodaetica et Cartographica Si... close
• Digital Humanities and Cultural Her... close

Zero-shot classification technology aims to acquire the ability to identify categories that do not appear in the training stage (unseen classes) by learning some categories of the data set (seen classes), which has important practical significance in the era of remote sensing big data. Until now, the zero-shot classification methods in remote sensing field pay little attention to the semantic space optimization after mapping, which results in poor classification performance. Based on this consideration, this paper proposed a zero shot remote sensing image scene classification method based on cross-domain mapping with auto-encoder and collaborative representation learning. In the supervised learning module, based on the class semantic vector of seen class and the scene image sample, the depth feature extractor learning and robust mapping from visual space to semantic space are realized. In the unsupervised learning stage, based on the class semantic vectors of all classes and the unseen remote sensing image samples, collaborative representation learning and k-nearest neighbor algorithm are used to modify the semantic vectors of unseen classes, so as to alleviate the problem of the shift of seen class semantic space and unseen class semantic space one after another and unseen after self coding cross domain mapping model mapping the shift of class semantic space and unseen class semantic space after collaborative representation. In the testing phase, based on the depth feature extractor, self coding cross domain mapping model and modified unseen class semantic vector, the classification of unseen class remote sensing image scene can be realized. We integrate a number of open remote sensing image scene data sets and build a new remote sensing image scene data set, experiments were conducted using this dataset The experimental results show that the algorithm proposed in this paper were significantly better than the existing zero shot classification method in the case of a variety of seen and unseen classes.

This paper is divided into three parts. The first part of the article is a brief review of the developmental history of ancient and modern cartography in China. It shows that China is one of the earliest countries in the world to create maps. China played an important role in the development of cartography in the world, and has made significant contributions to the development of cartography. Up till the Ming Dynasty in 15th Century, China's map and cartography had always been ahead of the Western countries, but fell behind only after the Qing Dynasty. The second part of the article describes the rapid development of China's mapping and cartography after the founding of the "New China", including the tremendous achievements in the areas of mapping and creation of national topographic maps, thematic maps and atlases, remote sensing cartography, computer-aided cartography, multimedia electronic maps, mobile communication maps, online maps, and theoretical research of cartography. China has caught up and reached the world's advanced level in cartography. The third part It proposes the direction and adjusted roles of mapping and cartography in China in the new century, and analyzes the opportunities, challenges and prospects of cartography in the era of big data, internet and artificial intelligence. Via visualization of 3D dynamic mapping, big data can show the spatial pattern and regional differentiation as well as temporal and spatial dynamic change of subjects and phenomena, and then make analysis and evaluation, forecast and early warning, zoning and layout, planning and design, management and regulation. Therefore cartography can play an important role in the era of big data. In the future, internet will become the main platform for map creation and application. Maps will be more popular, personalized, intelligent and practical.