Building simplification is an important research area in automatic map generalization. Up to now, many approaches have been proposed by scholars. However, in the continuous transformation of scales for buildings, keeping the main shape characteristics, area, and orthogonality of buildings are always the key and difficult points. Therefore, this paper proposes a method of progressive simplification for buildings based on structural subdivision. In this paper, iterative simplification is adopted, which transforms the problem of building simplification into the simplification of the minimum details of building outlines. Firstly, a top priority structure (TPS) is determined, which represents the smallest detail in the outline of the building. Then, according to the orthogonality and concave–convex characteristics, the TPS are classified as 62 subdivisions, which cover the local structure of the building polygon. Then, the subdivisions are divided into four simplification types. The building is simplified to eliminate the TPS continuously, retaining the right-angle characteristics and area as much as possible, until the results satisfy the constraints and rules of simplification. A topographic dataset (1:1 K) collected from Kadaster was used for our experiments. In order to evaluate the algorithm, many tests were undertaken, including tests of multi-scale simplification and simplification of typical buildings, which indicate that this method can realize multi-scale presentation of buildings. Compared with the existing simplification methods, the comparison results show that the proposed method can simplify buildings effectively, which has certain advantages in keeping shape characteristics, area, and rectangularity.

The ditch pattern reflects the distribution characteristics of an agricultural drainage system and needs to be detected to enrich the data source before map generalization. Due to several breaks, the connectivity of the ditches is destroyed between ditches and rivers in map representation, making its structure complex. Previous studies have primarily focused on extracting parallel ditches, paying less attention to complex ditches with breaks. The pattern recognition of ditches does not merely involve the extraction of parallel relation. It involves the extraction of different level relations, which is a great challenge. Therefore, this study proposes a novel model to describe the complex structure of ditches. Our work consisted of the following three main contributions: (1) the collinear relation was defined to detect the groups of ditch segments separated by breaks, (2) the detection method of parallel relation was improved throughout the multi-parameter combined constraints, and (3) the main-tributary relation was proposed to build a connection between parallel groups and rivers. The experimental results showed that the proposed method was solved effectively in complex ditch pattern recognition.

Map compilation is a fundamental component of cartography. Apart from vector maps, satellite image maps also require compilation for specific purposes, such as enhancing visual clarity, concealing sensitive information, and ensuring consistency with corresponding vector representations. This paper proposes an automated method for satellite image map compilation based on deep learning and guided by vector map data, which consists of several key steps. First, scale-matched vector maps and satellite image maps are aligned and partitioned to generate training samples in the form of paired vector tiles and satellite image patches. Second, a base satellite image generation model with an encoder-decoder-type deep learning architecture is constructed and trained on these sample pairs to learn the mapping from vector data to realistic satellite imagery. Third, geographic objects designated for compilation are identified, and transfer learning is performed to fine-tune the base model, which improves its sensitivity to regions requiring modification. Subsequently, specific compilation operations—deletion, insertion, distortion, and displacement—are defined; corresponding vector features are then edited using analogous transformations before being input into the trained model to generate updated satellite images reflecting the intended changes. The proposed method enables both selective and operation-diverse compilation of geographic objects in satellite image maps. Experiments conducted using real-world datasets verify the capability of the proposed method to compile linear and polygonal geographic objects through various operations.