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Lars Arge

Learning to find hydrological corrections

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

DOI

High resolution Digital Elevation models, such as the 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, before they can be used in realistic flow modeling. This include removal of bridges, which otherwise act as dams in flow modeling, and inclusion of culverts that transport water underneath roads. For this reason, there is list of known hydrological corrections for the danish model. However, producing this list is a slow an expensive process, since it is to a large extent done manually, often with only local input. In this paper we propose a new algorithmic approach based on machine learning and convolutional neural networks for automatically detecting hydrological corrections on large terrain data. Our model is able to detect most known hydrological corrections and quite a few more that should have been included in the original list.

Original languageEnglish
Title of host publication27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL '19)
EditorsFarnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Güting, Lars Kulik, Shawn Newsam
Number of pages4
PublisherAssociation for Computing Machinery
Publication year2019
Pages464-467
ISBN (Electronic)9781450369091
DOIs
Publication statusPublished - 2019
Event27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 - Chicago, United States
Duration: 5 Nov 20198 Nov 2019

Conference

Conference27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
LandUnited States
ByChicago
Periode05/11/201908/11/2019
SponsorApple, DiDi, Esri, et al, Here, Microsoft
SeriesGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

    Research areas

  • Flow modelling, Geographic information systems, Hydrological conditioning, Neural networks

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