A building outline automatic aggregation method, device, equipment and storage medium

Abstract

The present application discloses a building outline automatic aggregation method, device, equipment and storage medium. The method includes: obtaining a set of contour parameters of buildings in the target area; classifying the buildings according to the spatial topological relationship based on the set of contour parameters according to the screening conditions to obtain a set of isolated buildings and a set of non-isolated buildings; The filtering conditions include a first filtering condition based on a building circumscribed rectangular frame and a second filtering condition based on a building outline; merging buildings with adjacent relationships in the non-isolated building set to obtain a merged building cluster; Based on the set of isolated buildings and all the combined building clusters, an outline aggregation result of the buildings in the target area is obtained. It can speed up the retrieval time of isolated buildings and is not limited by application scenarios. On the basis of ensuring the accuracy and reliability of clustering results, it improves the efficiency of building outline clustering.

Description

Technical field [0001] The present invention relates to the field of spatial clustering, and in particular, to a building contour automatic polymerization method, apparatus, device, and storage medium. Background technique [0002] Geography Information System (GIS) is an emerging marginal discipline integrating computer science, surveying and mapping remote induction, geography, environmental science, spatial science, information science and management science. Spatial analysis is the core and soul of GIS, but also an important component of achieving spatial data value. Spatial analysis with the properties information of spatial data, can provide powerful, rich spatial data query features. Spatial clustering is an important part of space analysis, refers to the composition of the object in the space data set by a similar or related object, with a higher similarity or the same feature, and there is a large difference between different classes. The cluster is a method of learning ...

AI Technical Summary

Problems solved by technology

[0004] In the prior art, each element is judged one by one based on the Delaunay triangulation or space directly according to the topological relationship, but the Delaunay triangulation method generally first needs to establish a triangulation for all nodes of the outline elements of the entire building, and build a network when the scale is large The time and space overhead is large, and it contains facets and complex face fusion operations, resulting in inefficiency

In the existing technology, clustering is also carried out based on the center point, spatial distance, area and other quantitative values, but the application scope is generally limited to large-scale spatial scenarios such as cartographic synthesis, and it is difficult to meet the needs of individual buildings in smaller scenarios. Apps that represent

In the prior art, feature points are also calculated by using additional data sources such as digital surface model (DSM, Digital Surface Model), digital elevation model (DEM, Digital Elevation Model) or digital orthophoto image (DOM, Digital OrthophotoMap), However, under real operating conditions, more data sources mean more cost and overhead, reducing the efficiency of contour aggregation

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