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Automatic determination of stream networks from DEMs by using road network data to locate culverts / Ville Mäkinen in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 1-2 (January - February 2019) . - pp 291 - 313 Titre : Automatic determination of stream networks from DEMs by using road network data to locate culverts Type de document : Article/Communication Auteurs : Ville Mäkinen, Auteur ; Juha Oksanen, Auteur ; Tapani Sarjakoski, Auteur ; Tapani Sarjakoski, Auteur Année de publication : 2019 Article en page(s) : pp 291 - 313 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection automatique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] écoulement des eaux [Termes IGN] Finlande [Termes IGN] itération [Termes IGN] modèle numérique de surface [Termes IGN] réseau hydrographique [Termes IGN] réseau routier [Termes IGN] semis de points [Termes IGN] Triangulated Irregular Network Résumé : (auteur) Determining stream networks automatically from digital elevation models is an issue that is actively being studied. The quality of elevation models has increased over time, but many hydrologically critical features, such as culverts, are often missing from the elevation data. To analyze the surficial water flow, one must either prepare a special elevation model or post-process an already-existing model. This study builds on the traditional, well-established method of determining the stream network from digital elevation models. We have extended the traditional method by locating culverts automatically, using road network data as an input. We show, by comparison to the reference data, that the culverts being most relevant for the stream network can be found with good accuracy. We demonstrate that by including the automatically located culverts in the automatic stream network determination, the quality of the generated network can be noticeably improved. Numéro de notice : A2019-021 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1530353 Date de publication en ligne : 11/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1530353 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91686 [article]

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