High resolution Digital Elevation models, such as the (Big) grid terrain model of Denmark with more than 200 billion measurements, is a basic requirement for water flow modelling and flood risk analysis. However, a large number of modifications often need to be made to even very accurate terrain models, such as the Danish model, before they can be used in realistic flow modeling. These modifications include removal of bridges, which otherwise will act as dams in flow modeling, and inclusion of culverts that transport water underneath roads. In fact, the danish model is accompanied by a detailed set of hydrological corrections for the digital elevation model. However, producing these hydrological corrections is a very slow an expensive process, since it is to a large extent done manually and often with local input. This also means that corrections can be of varying quality. In this paper we propose a new algorithmic apporach based on machine learning and convolutional neural networks for automatically detecting hydrological corrections for such large terrain data. Our model is able to detect most hydrological corrections known for the danish model and quite a few more that should have been included in the original list.
When handling large datasets that exceed the capacity of the main memory, movement of data between main memory and external memory (disk), rather than actual (CPU) computation time, is often the bottleneck in the computation. Since data is moved between disk and main memory in large contiguous blocks, this has led to the development of a large number of I/O-efficient algorithms that minimize the number of such block movements. However, actually implementing these algorithms can be somewhat of a challenge since operating systems do not give complete control over movement of blocks and management of main memory.TPIE is one of two major libraries that have been developed to support I/O-efficient algorithm implementations. It relies heavily on the fact that most I/O-efficient algorithms are naturally composed of components that stream through one or more lists of data items while producing one or more such output lists, or components that sort such lists. Thus TPIE provides an interface where list stream processing and sorting can be implemented in a simple and modular way without having to worry about memory management or block movement. However, if care is not taken, such streaming-based implementations can lead to practically inefficient algorithms since lists of data items are typically written to (and read from) disk between components.In this paper we present a major extension of the TPIE library that includes a pipelining framework that allows for practically efficient streaming-based implementations while minimizing I/O-overhead between streaming components. The framework pipelines streaming components to avoid I/Os between components, that is, it processes several components simultaneously while passing output from one component directly to the input of the next component in main memory. TPIE automatically determines which components to pipeline and performs the required main memory management, and the extension also includes support for parallelization of internal memory computation and progress tracking across an entire application. Thus TPIE supports efficient streaming-based implementations of I/O-efficient algorithms in a simple, modular and maintainable way. The extended library has already been used to evaluate I/O-efficient algorithms in the research literature and is heavily used in I/O-efficient commercial terrain processing applications by the Danish startup SCALGO.
An important problem in terrain analysis is modeling how water flows across a terrain creating floods by forming channels and filling depressions. In this paper 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 which 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 paper contains four main algorithmic results: (i) An internal-memory algorithm for answering terrain-flow queries: preprocess Σ into a linear-size data structure so that 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 that 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 \(\mathsf {P} \) in Σ and flow rate along \(\mathsf {P} \) , 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.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.