Identifying the spatial configurations of buildings and grouping them reasonably is an important task in cartography. This study developed a grouping approach using graph deep learning by integrating ...multiple cognitive features and manual cartographic experiences. Taking building center points as nodes, adjacent buildings were organized as a graph in which cognitive variables including size, orientation, and shape were defined for each node. Then, a learning model combining the graph convolution and neural network was designed to analyse the adjacent buildings modelled by the graph. The center points of groups were used as labels to train the positions of graph nodes and finally, a k-means algorithm was employed to obtain the grouping results based on the predicted node positions. Experiments confirmed that our approach can extract the inherent features describing the grouping relationship between buildings and performed better than two existing approaches referring to the ARI index (from 0.647 to 0.749).
Cartographic generalization is a process similar to text summarization that transforms a map when scale is reduced. Cartographic generalization simplifies the map content while preserving as much as ...possible the initial characteristics and spatial relations of the map. The automation of this process requires a deep understanding of the context of each map feature, which involves different criteria such as the shape of the feature, the semantic nature of the feature, or the spatial patterns of its neighbouring features. This is why multiple criteria decision techniques can be relevant during the process. This paper proposes two use cases of cartographic generalization sub-tasks where multiple criteria decision techniques improve past techniques: the classification of urban building blocks and the ordering of the buildings to keep in the map as a priority. In both use cases, the proposed methods are experimented on large real cartographic datasets, and evaluated in comparison to alternative techniques.
This paper proposes a model to quantify the multiscale representation of a polyline based on iterative head/tail breaks. A polyline is first transformed into a corresponding Fourier descriptor ...consisting of normalized Fourier-series-expansion coefficients. Then, the most significant finite components of the Fourier descriptor are selected and ranked to constitute the polyline constrained Fourier descriptor. Using Shannon's information theory, information content of the constrained Fourier-descriptor components is defined. Next, head/tail breaks are introduced to iteratively divide the constrained Fourier descriptor into head and tail components according to the heavy-tailed distribution of information contents. Thus, simplified polylines are reconstructed using ordered heads generated from head/tail breaks. Finally, the radical law is introduced and applied to model multiscale polyline representation by quantifying the scale of each simplified polyline. Three experiments are designed and conducted to evaluate the proposed model. The results demonstrate that the proposed model is valid and efficient for quantifying multiscale polyline representation.
Multilane roads are a set of approximately parallel line segments representing the same road in large-scale vector maps. They must be extracted first in cartographic generalization. There are ...numerous multilane roads in the easily accessible OpenStreetMap (OSM) dataset. For this dataset, polygon-based methods have achieved state-of-the-art performance. However, traditional polygon-based methods usually rely on manually labeled data, which means they are time-consuming and labor-intensive. To address this problem, an unsupervised framework for extracting multilane roads is proposed in this study. Road segments were first grouped to form the road polygons. A set of shape descriptors was formulated to reduce the dimensions of individual road polygons into conceptual points. Next, dimensional shape descriptors were standardized using logarithmic standardization. The density peaks clustering (DPC) algorithm was employed to classify these points. Then, cluster tags were identified manually to recognize which clusters represent multilane polygons. Finally, post-processing learning from the concept of assimilation is proposed to fill holes and remove islands. Experiments were conducted to extract multilane roads with datasets from three cities: Wuhan, Beijing and Munich. The experimental results show that the proposed framework effectively extracted multilane roads without any labels with accuracy levels comparable to those of supervised methods.
Aiming at the problem that the current double direction buffering algorithm is easy to use to seal the “bottleneck” area when simplifying coastlines, an adaptive simplification method for coastlines ...using a skeleton line “bridge” double direction buffering algorithm is proposed. Firstly, from the perspective of visual constraints, the relationship between the buffer distance and the coastline line width and the minimum recognition distance of the human eye is theoretically derived and determined. Then, based on the construction of the coastline skeleton binary tree, the “bridge” skeleton line is extracted using the “source tracing” algorithm. Finally, the shoreline adaptive simplification is realized by constructing a visual buffer of “bridge” skeleton lines to bridge the original resulting coastline and the local details. The experimental results show that the proposed method can effectively solve the problem that the current double direction buffering algorithm has, which can significantly improve the quality of simplification.
Lane-level road cluster is a most representative phenomenon in road networks and is vital to spatial data mining, cartographic generalization, and data integration. In this article, a lane-level road ...cluster recognition method was proposed. First, the conception of lane-level road cluster and our motivation were addressed and the spatial characteristics were given. Second, a region growing cluster algorithm was defined to recognize lane-level road clusters, where constraints including distance and orientation were used. A novel moving distance (MD) metric was proposed to measure the distance of two lines, which can effectively handle the non-uniformly distributed vertexes, heterogeneous length, inharmonious spatial alignment, and complex shape. Experiments demonstrated that the proposed method can effectively recognize lane-level road clusters with the agreement to human spatial cognition.
This study focuses on a skeleton line extraction algorithm of areal hydrographic elements for cartographic generalization. The requirements of skeleton line extraction for areal hydrographic elements ...were first analysed and a new automatic extraction algorithm that considered spatial and hierarchical relationships was proposed to meet these requirements. A graph with structured areal hydrographic elements was subsequently presented, and the graph pruning operation was conducted based on the spatial relationship and pruning length threshold. The skeleton line connection of the traditional unconstrained edge midpoint algorithm was improved by the guidance of spatial and hierarchical relationships. Finally, the proposed method was used to extract skeleton lines from 1:250,000 hydrographic elements, and compared with other algorithms. The experiments demonstrate that the skeleton lines extracted by the proposed method have certain advantages in natural continuity, hierarchy, and topological consistency; the results are closer to those of manual extraction and avoiding the subsequent optimization process.
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