Abstract: Disclosed is a graph convolution auto-encoder based method for computing road network similarity. The method includes: creating a dual graph of a road network, and giving road network space feature information to nodes of the dual graph from three aspects of global, local and connection characteristics on the basis of a relation principle between an entire structure and parts of the structure, such that a quantitative expression of a road network graph structure is obtained; aggregating and updating node feature information and structure information of a road network graph by the graph convolution auto-encoder, and forming a deep understanding of the road network, such that a coded expression of node information of the road network is obtained; and mapping a complex high-dimensional feature space to an easy-to-measure low-dimensional feature space through an average pooling operation, so as to obtain a set of feature vectors, and computing the similarity.

Abstract: Disclosed in the present disclosure is a similarity calculation method considering multiple features for a multi-scale road network, which includes structural similarity calculation of skeleton lines and local feature similarity calculation of the multi-scale road network. A road network stroke is generated, skeleton lines of the road network are extracted and transformed into a structure tree, and the skeleton similarity of the road network is evaluated by calculating structural similarity of the structure tree of the multi-scale road network. Topological similarity of the multi-scale road network is calculated by using a difference matrix of a conceptual domain graph of road meshes, geometric similarity of the multi-scale road network is calculated by using a density of road meshes, and local similarity of the multi-scale road network is obtained by integrating the hierarchy of the road network into the topological and geometric similarity calculation in the form of matrices.

Abstract: Disclosed in the present disclosure is a commutative encryption and watermarking method based on a chaotic system and a zero watermark for vector geospatial data. According to the method, firstly, the vector geospatial data are scrambled and encrypted by using chaotic sequences generated by a composite chaotic system. Then, vector geospatial elements are randomly combined in pairs. A feature matrix is constructed according to the number of vertex coordinates of the vector geospatial elements in combinations, and the parity of the number. Finally, an XOR operation is performed on the feature matrix and the watermark image to construct a zero watermark image, and the zero watermark is constructed through invariant features of the vector geospatial data.

Abstract: Disclosed in the present disclosure is a classification method based on skeleton lines for building shapes. The method includes: (1) expanding a template library of buildings by combining building shape classification in architecture and building shape features in real life on the basis of inheriting advantages of a traditional template matching method; (2) avoiding influence of small depressions and protrusions of the buildings on overall shapes by constructing a least-squares template; (3) extracting the skeleton lines of the buildings and calculating feature vectors of the buildings; and (4) calculating similarity between feature vectors of skeleton lines of the buildings and skeleton lines of templates by using cosine similarity, and selecting the template with the highest similarity as a classification result of the building shapes.

Abstract: The disclosure provides a reversible watermarking method for the oblique photography three-dimensional models with controllable accuracy. According to the disclosure: firstly, by using the global stability of the average included angle of vertex normal vector, the feature points of the oblique photography three-dimensional model are extracted; secondly, the mapping relationship is established by the ratio of the distance between feature points and non-feature points, and the vertexes are grouped, and each group consists of a feature point corresponding to several non-feature points; finally, the spherical coordinate system is constructed by taking the feature points as the coordinate origin in grouping, and the watermark is embedded by modifying the radius of the coordinate system.