On scalable DCEL overlay operations

The Doubly Connected Edge List (DCEL) is an edge-list structure widely used in spatial applications, primarily for planar topological and geometric computations. However, it is also applicable to various types of data, including 3D models and geographic data. An essential operation is the overlay op...

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Bibliographic Details
Published in:GeoInformatica Vol. 29; no. 3; pp. 751 - 788
Main Authors: Calderon-Romero, Andres, Abdelhafeez, Laila, Trajcevski, Goce, Magdy, Amr, Tsotras, Vassilis J.
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2025
Springer Nature B.V
Subjects:
Art galleries & museums
Computer Science
Data Structures and Information Theory
Datasets
Geographical Information Systems/Cartography
Information Storage and Retrieval
Multimedia Information Systems
Polygonization
Polygons
Queries
Spatial data
Three dimensional models
United States > US
Spatial data structures
DCEL
Overlay operator
ISSN:1384-6175, 1573-7624
Online Access:Get full text
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Summary:The Doubly Connected Edge List (DCEL) is an edge-list structure widely used in spatial applications, primarily for planar topological and geometric computations. However, it is also applicable to various types of data, including 3D models and geographic data. An essential operation is the overlay operation , which combines the DCELs of two input polygon layers and can easily support spatial queries on polygons like the intersection, union, and difference between these layers. However, existing techniques for spatial overlay operations suffer from two main limitations. First, they fail to handle many large datasets practically used in real applications. Second, they cannot handle arbitrary spatial lines that practically form polygons, e.g., city blocks, but they are given as a set of scattered lines. This work proposes a distributed and scalable way to compute the overlay operation and its related supported queries. Our operations also support arbitrary spatial lines through a scalable polygonization process. We address the issues of efficiently distributing the lines and overlay operators and offer various optimizations that improve performance. Our experiments demonstrate that the proposed scalable solution can efficiently compute the overlay of large real datasets.
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ISSN:1384-6175
1573-7624
DOI:10.1007/s10707-025-00539-x