## 2 Basic Ideas
There is a dialectical relationship between the tasks of geographic scenes understanding at different semantic layers. The input of geographic scene understanding is the original HSR remote sensing images, and the output is semantic information of the geographic scene. In the tasks of geographic scene understanding, it is necessary to combine the semantic processing of the visual layer and object layer (feature extraction and target recognition) with the semantic reasoning of the concept layer (scene description and classification), the different tasks are interdependent on each other [16]. The cognition of visual and object layer semantics can form the inference of concept layer semantics, and the cognition of concept layer semantics can be used as knowledge to guide the extraction of visual and object layer semantics. Figure 1: Image characteristics of geographic scenes in HSR remote sensing: (**a**) reflects the diversity of the ground objects in the geographic scene; (**b**) reflects the diversity of imaging conditions of HSR remote sensing images; (**c**) reflects the differences in the types of ground objects in the same category of geographic scenes; (**d**) reflects the similarity in the types of ground objects in different categories of geographic scenes. Figure 2: Semantic hierarchy model of three layers structure. (2) Spatial structure characteristics play an important role in the geographic scene understanding of HSR remote sensing images. Because of the global and polysemy of the geographic scene, the geographic scene understanding is not a simple stacking of some local semantics [17]. In the same category of the geographic scene, the objects of the same type have similar individual characteristics and spatial distribution patterns. However, there are different structural features among different categories of geographic scenes. In Figure 3, these two geographic scenes of \"residential area\" and \"industrial area\" contain similar visual features and object types, which are composed of buildings, roads and vegetation. However, there are great differences in spatial structures between objects, which is a critical factor to distinguish the categories of geographic scenes. Therefore, the spatial structure characteristics of geographic scenes are relatively stable, and making full use of spatial information such as geometry, texture and context of HSR remote sensing images is an effective way to improve the understanding of geographic scenes [18; 19]. (3) The data characteristics of HSR remote sensing images have both opportunities and challenges for the geographic scene understanding. The amount of HSR remote sensing images increases significantly. As the spatial resolution increases, the area of the ground covered by each pixel decreases significantly. This makes the ground object details and spatial distribution of HSR remote sensing images clearer. Compared with medium-low-resolution remote sensing images, HSR remote sensing images can be interpreted at the scene level, where semantic information is more abstract. However, compared with natural images, the HSR remote sensing images used for geographic scene understanding are less accessible in terms of data availability, except for the differences in shooting distance, shooting angle and imaging sensors. Natural images can be easily and quickly obtained from the Internet, and a large amount of data has been given the relevant label information when uploaded to the Internet [20]. For example, the ImageNet dataset [21; 22] contains more than 14 million labeled samples in 1000 categories. HSR remote sensing images are not freely available for political, military and security reasons. HSR remote sensing images often rely on professional interpretation or even field research to obtain the correct label, the available sample size is limited. For instance, the UC-Merced dataset contains only 21 categories, with a total of 2100 labeled samples.