Finding shortest paths on terrains by killing two birds with one stone

Author:

Kaul Manohar1,Wong Raymond Chi-Wing2,Yang Bin1,Jensen Christian S.1

Affiliation:

1. Aarhus University

2. The Hong Kong University of Science and Technology

Abstract

With the increasing availability of terrain data, e.g., from aerial laser scans, the management of such data is attracting increasing attention in both industry and academia. In particular, spatial queries, e.g., k -nearest neighbor and reverse nearest neighbor queries, in Euclidean and spatial network spaces are being extended to terrains. Such queries all rely on an important operation, that of finding shortest surface distances. However, shortest surface distance computation is very time consuming. We propose techniques that enable efficient computation of lower and upper bounds of the shortest surface distance, which enable faster query processing by eliminating expensive distance computations. Empirical studies show that our bounds are much tighter than the best-known bounds in many cases and that they enable speedups of up to 43 times for some well-known spatial queries.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. On Efficient Shortest Path Computation on Terrain Surface: A Direction-Oriented Approach;IEEE Transactions on Knowledge and Data Engineering;2024-08

2. Efficient Shortest Path Queries on 3D Weighted Terrain Surfaces for Moving Objects;2024 25th IEEE International Conference on Mobile Data Management (MDM);2024-06-24

3. Routing with Massive Trajectory Data;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

4. Proximity Queries on Point Clouds using Rapid Construction Path Oracle;Proceedings of the ACM on Management of Data;2024-03-12

5. 3D-Polishing for Triangular Mesh Compression of Point Cloud Data;Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2023-08-04

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