A Progressive Simplification Method for Buildings Based on Structural Subdivision

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

Hierarchical Area Partitioning Method of Urban Road Networks Matching

In view of the "Node-Arc" data model of road network in the aspect of structured expressing the deficiencies,the hierarchical area partitioning of road network based on the principle of stroke,which made road network space structure characteristics of the expression with the hierarchical feature was designed.Based on road hierarchy and connected relationship with the area domain boundaries,the road in the area was hierarchically divided.A hierarchical model was established based on "whole-part-object" data model.Finally,the model of urban road network matching is proposed,which used consistency evaluation model selected matching objects from high-grade road to the low-level road.The experiment results indicated that the method was suitable to solve the road matching problem with typical urban features

Automatic Matching of Multi-scale Road Networks under the Constraints of Smaller Scale Road Meshes

A new method is proposed to achieve automatic matching for multi-scale roads under the constraints of the smaller scale data. Firstly, meshes should be extracted from the two different scales road data. Secondly, several basic meshes in the larger scale road network will be merged as a composite one, which will be matched with one mesh from the smaller scale road network, so that the meshes with many-to-one and one-to-one matching relationships will be matched. Thirdly, meshes from the two different scale road data with many-to-many matching relationships will be matched. Finally, road will be classified into two categories under the constraints of meshes: mesh border roads and mesh internal roads, and then matching will be done in their own categories according to the matching relationships between the two scales meshes. The results showed that roads from different scale will be more precisely matched

A detection method for road network interchanges with the MeshCNN based on Delaunay triangulation

Due to their unstructured characteristics, mature convolutional neural network (CNN) models often have difficulty performing spatial analysis with vector data. Current studies used graph neural network (GCN) models to address this problem. However, the definition of cognition factors involves uncertainties, making it challenging to accurately and comprehensively define these factors. In this paper, the road interchange detection task is taken as an example to introduce the MeshCNN, a deep learning model based on triangular mesh data, aiming to provide a new solution for spatial analysis of vector data. A triangular edge classification model is first trained with simple input features. Then, interchanges are detected based on the classification results with an adaptive method. Experiments were conducted on real-world road network data from four cities. The results reveal that the proposed method outperformed the existing methods with precision and recall rate of 89.36% and 79.25% for interchange detection on the total datasets. Furthermore, our proposed method can also detect interchanges in other regions more easily than the GCN method

AFL-Net: Attentional Feature Learning Network for Building Extraction from Remote Sensing Images

Convolutional neural networks (CNNs) perform well in tasks of segmenting buildings from remote sensing images. However, the intraclass heterogeneity of buildings is high in images, while the interclass homogeneity between buildings and other nonbuilding objects is low. This leads to an inaccurate distinction between buildings and complex backgrounds. To overcome this challenge, we propose an Attentional Feature Learning Network (AFL-Net) that can accurately extract buildings from remote sensing images. We designed an attentional multiscale feature fusion (AMFF) module and a shape feature refinement (SFR) module to improve building recognition accuracy in complex environments. The AMFF module adaptively adjusts the weights of multi-scale features through the attention mechanism, which enhances the global perception and ensures the integrity of building segmentation results. The SFR module captures the shape features of the buildings, which enhances the network capability for identifying the area between building edges and surrounding nonbuilding objects and reduces the over-segmentation of buildings. An ablation study was conducted with both qualitative and quantitative analyses, verifying the effectiveness of the AMFF and SFR modules. The proposed AFL-Net achieved 91.37, 82.10, 73.27, and 79.81% intersection over union (IoU) values on the WHU Building Aerial Imagery, Inria Aerial Image Labeling, Massachusetts Buildings, and Building Instances of Typical Cities in China datasets, respectively. Thus, the AFL-Net offers the prospect of application for successful extraction of buildings from remote sensing images