Inferring High-level Geographical Concepts via Knowledge Graph and Multi-scale Data Integration: A Case Study of C-shaped Building Pattern Recognition

by   Zhiwei Wei, et al.

Effective building pattern recognition is critical for understanding urban form, automating map generalization, and visualizing 3D city models. Most existing studies use object-independent methods based on visual perception rules and proximity graph models to extract patterns. However, because human vision is a part-based system, pattern recognition may require decomposing shapes into parts or grouping them into clusters. Existing methods may not recognize all visually aware patterns, and the proximity graph model can be inefficient. To improve efficiency and effectiveness, we integrate multi-scale data using a knowledge graph, focusing on the recognition of C-shaped building patterns. First, we use a property graph to represent the relationships between buildings within and across different scales involved in C-shaped building pattern recognition. Next, we store this knowledge graph in a graph database and convert the rules for C-shaped pattern recognition and enrichment into query conditions. Finally, we recognize and enrich C-shaped building patterns using rule-based reasoning in the built knowledge graph. We verify the effectiveness of our method using multi-scale data with three levels of detail (LODs) collected from the Gaode Map. Our results show that our method achieves a higher recall rate of 26.4 compared to existing approaches. We also achieve recognition efficiency improvements of 0.91, 1.37, and 9.35 times, respectively.


page 17

page 19

page 22

page 27

page 31


Linear building pattern recognition via spatial knowledge graph

Building patterns are important urban structures that reflect the effect...

ArtGraph: Towards an Artistic Knowledge Graph

This paper presents our ongoing work towards ArtGraph: an artistic knowl...

A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale Learning

In modern machine learning, pattern recognition replaces realtime semant...

Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding

Modeling of relation pattern is the core focus of previous Knowledge Gra...

Spatial Pattern Recognition with Adjacency-Clustering: Improved Diagnostics for Semiconductor Wafer Bin Maps

In semiconductor manufacturing, statistical quality control hinges on an...

Visual Estimation of Building Condition with Patch-level ConvNets

The condition of a building is an important factor for real estate valua...

Automated Lane Detection in Crowds using Proximity Graphs

Studying the behavior of crowds is vital for understanding and predictin...

Please sign up or login with your details

Forgot password? Click here to reset