## 6 Open Problems and Challenges  
1. Integrated system engineering for geographic scene understanding The research of HSR remote sensing images is often only used the visual information and a little semantic information, such as target detection, image classification, image segmentation, scene classification and so on. These researchers can often only detect a certain target contained in the image, or obtain the category labels of each pixel or the whole image, but they do not make full use of the features of the image. Thus, it is difficult to mine the attributes, characteristics and relationships among the objects in the image in detail. In this way, images are not fully understood at the semantic level and HSR remote sensing data are not fully utilized. The organic integration of single subtask or feature information of HSR remote sensing image processing can enhance the performance of understanding, and it is more suitable for people's understanding mode of the geographic scene. The multi-class feature information and multi-subtasks are not completely independent, and the mutual influence and restriction factors should be considered comprehensively (Figure 8). Therefore, the construction of system engineering for geographic scene understanding can follow the following Formula (1): \[Y=\{(feature_{1}\oplus feature_{2}\oplus\ldots\oplus feature_{n})\otimes(task_ {1}\oplus task_{2}\oplus\ldots\oplus task_{n})\}\] (1) In the formula, \(Y\) is the system engineering for geographic scene understanding, \(\otimes\) and \(\oplus\) represent the different combinations of features and tasks. 2. Comprehensive semantic representation of the geographic scene in HSR remote sensing image  
The purpose of geographic scene understanding based on HSR remote sensing image is to semantically explain the content at all layers in the geographic scene. It is necessary to construct a comprehensive semantic representation model of geographic scenes, and to standardize and integrate the various layers and types of semantic information obtained from the understanding of geographic scenes. The semantic parsing tree of a geographic scene can be constructed, and the tree structure of and/or a graph can be used to represent the semantic content of understanding. The semantic parsing tree of the geographic scene follows a unified semantic specification, which is generally divided into four levels \"scene-object-part-pixel\" (Figure 9). The \"And\" node represents the decomposition, such as \"scene\(\rightarrow\)object\", \"object\(\rightarrow\)part\" and so on, which is followed the syntactic rules of \"A\(\rightarrow\)BCD\". Any geographic scene semantics can be represented by this parsing tree structure, and the semantic hierarchy of geographic scene is clearly divided, which has both semantic attributes and semantic relations between different levels. (3) Adaptability for large-scale complex geographic scenes  
With the massive growth of image data and the continuous subdivision of scene categories, the problem of geographic scene understanding is faced with unprecedented challenges both in image quantity and scene category. The understanding of real geographic scenes requires higher complexity and depth than scene classification, and the solution to this problem will have a profound impact on artificial intelligence technology. For the current semantic understanding approaches of geographic scenes, there are still the  
Figure 8: System engineering framework for geographic scene understanding based on HSR remote sensing image. Figure 9: Hierarchical representation structure of the semantic of geographic scene. following problems to be solved: (1) The semantic extraction ability of the visual layer is insufficient. Even considering multiple visual features, most of them are the simple superposition of different features. (2) The semantic modeling of object layer is redundant and lacks homogeneity in the description, which makes it difficult to take into account the computational efficiency and effect.