地形上的1D和2D流路由

IF 1.2 Q4 REMOTE SENSING ACM Transactions on Spatial Algorithms and Systems Pub Date : 2022-06-02 DOI:10.1145/3539660
L. Arge, Aaron Lowe, Svend C. Svendsen, P. Agarwal
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引用次数: 0

摘要

地形分析中的一个重要问题是模拟水如何通过形成沟渠和填满洼地流过地形,从而产生洪水。在本文中,我们研究了许多与流量查询相关的问题:给定地形Σ,表示为具有n个顶点的三角形xy单调表面,以及可能随时间变化的降雨分布R,确定在给定顶点或边缘上流过多少水作为时间的函数。我们为流查询开发了内存和I/ o高效算法。本文包含四个主要的算法结果:(i)用于回答地形流查询的内存算法:将Σ预处理为线性大小的数据结构,因此给定雨分布R,可以快速报告Σ的所有顶点和边的流量函数。(ii)用于回答地形流查询的I/ o高效算法。(iii)回答顶点流查询的内存算法:将Σ预处理成一个线性大小的数据结构,给定一个雨分布R,可以快速计算出单流向(SFD)模型下顶点的流量函数。(iv)一种有效的算法,给定Σ中的路径𝖯和𝖯的流速,计算水沿其流动的二维通道。此外,我们实现了地形流查询和2D通道算法的一个版本,并在真实地形上检查了许多查询。
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1D and 2D Flow Routing on a Terrain
An important problem in terrain analysis is modeling how water flows across a terrain creating floods by forming channels and filling depressions. In this article, we study a number of flow-query-related problems: Given a terrain Σ, represented as a triangulated xy-monotone surface with n vertices, and a rain distribution R that may vary over time, determine how much water is flowing over a given vertex or edge as a function of time. We develop internal-memory as well as I/O-efficient algorithms for flow queries. This article contains four main algorithmic results: (i) An internal-memory algorithm for answering terrain-flow queries: Preprocess Σ into a linear-size data structure so given a rain distribution R, the flow-rate functions of all vertices and edges of Σ can be reported quickly. (ii) I/O-efficient algorithms for answering terrain-flow queries. (iii) An internal-memory algorithm for answering vertex-flow queries: Preprocess Σ into a linear-size data structure so given a rain distribution R, the flow-rate function of a vertex under the single-flow direction (SFD) model can be computed quickly. (iv) An efficient algorithm that, given a path 𝖯 in Σ and flow rate along 𝖯, computes the two-dimensional channel along which water flows. Additionally, we implement a version of the terrain-flow query and 2D channel algorithms and examine a number of queries on real terrains.
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来源期刊
CiteScore
4.40
自引率
5.30%
发文量
43
期刊介绍: ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.
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