{"@context": "https://schema.org", "@type": "Dataset", "@id": "https://doi.org/10.5878/jrex-z325", "identifier": [{"@type": "PropertyValue", "unitCode": "DOI", "value": "https://doi.org/10.5878/jrex-z325"}, {"@type": "PropertyValue", "unitCode": "https://www.rikstermbanken.se/termposter/1848/diarienummer", "unitText": "principalRegistrationNumber", "value": "SLU.seksko.2024.4.4.I\u00c4-1"}, {"@type": "PropertyValue", "unitCode": "DOI", "value": "https://doi.org/10.5878/gp08-ed29"}], "version": "1", "name": [{"@value": "Automatic Detection of Ditches and Natural Streams from Digital Elevation Models Using Deep Learning", "@language": "en"}, {"@value": "Automatisk detektion av diken och vattendrag fr\u00e5n digitala h\u00f6jdmodeller med hj\u00e4lp av djupinl\u00e4rning", "@language": "sv"}], "description": [{"@value": "This data contains the digital elevation models and polyline shapefiles with the location of channels from the 12 study areas used in this study. It also has the code to generate the datasets used to train the deep learning models to detect channels, ditches, and streams, and calculate the topographic indices. The code to train the models is also included, along with the models with the highest performance in 0.5 m resolution. The channels were mapped differently based on their type: ditches were manually digitized based on the visual analysis of some topographic indices and orthophotos obtained from the DEM. Streams were mapped by initially detecting all natural channel heads, then tracing the downstream channels, and finally manually editing them based on orthophotos.", "@language": "en"}, {"@value": "Den h\u00e4r datam\u00e4ngden inneh\u00e5ller digitala h\u00f6jdmodeller och polyline-shapefiler med placeringen av kanaler fr\u00e5n de 12 studieomr\u00e5dena som anv\u00e4nds i denna studie. Den inneh\u00e5ller ocks\u00e5 koden f\u00f6r att generera datam\u00e4ngderna som anv\u00e4nds f\u00f6r att tr\u00e4na djupinl\u00e4rningsmodellerna som anv\u00e4nds f\u00f6r att kartera kanaler, diken och vattendrag, samt ber\u00e4kna topografiska index. Koden f\u00f6r att tr\u00e4na modellerna ing\u00e5r ocks\u00e5, tillsammans med modellerna med b\u00e4st prestanda i 0,5 meters uppl\u00f6sning. Kanalerna kartlades p\u00e5 olika s\u00e4tt: diken digitaliserades manuellt baserat p\u00e5 visuell analys av vissa topografiska index och ortofoton som erh\u00e5llits fr\u00e5n en digital h\u00f6jdmodell. Vattendrag kartlades genom att f\u00f6rst uppt\u00e4cka alla naturliga kanalhuvuden, sedan sp\u00e5ra nedstr\u00f6mskanalerna och slutligen redigera dem manuellt baserat p\u00e5 ortofoton.", "@language": "sv"}], "creator": [{"@type": "Person", "@id": "https://orcid.org/0000-0002-4399-0804", "sameAs": "https://orcid.org/0000-0002-4399-0804", "name": "Mariana dos Santos Toledo Busarello", "email": "mariana.busarello@slu.se", "affiliation": [{"@type": "Organization", "name": [{"@value": "Swedish University of Agricultural Sciences, Department of Forest Ecology and Management", "@language": "en"}, {"@value": "Sveriges lantbruksuniversitet, Institutionen f\u00f6r skogens ekologi and sk\u00f6tsel", "@language": "sv"}], "@id": "https://ror.org/02yy8x990"}]}, {"@type": "Person", "@id": "https://orcid.org/0000-0001-5780-5596", "sameAs": "https://orcid.org/0000-0001-5780-5596", "name": "William Lidberg", "email": "william.lidberg@slu.se", "affiliation": [{"@type": "Organization", "name": [{"@value": "Swedish University of Agricultural Sciences, Department of Forest Ecology and Management", "@language": "en"}, {"@value": "Sveriges lantbruksuniversitet, Institutionen f\u00f6r skogens ekologi and sk\u00f6tsel", "@language": "sv"}], "@id": "https://ror.org/02yy8x990"}]}, {"@type": "Person", "@id": "https://orcid.org/0000-0002-6758-3971", "sameAs": "https://orcid.org/0000-0002-6758-3971", "name": "Anneli \u00c5gren", "email": "anneli.agren@slu.se", "affiliation": [{"@type": "Organization", "name": [{"@value": "Swedish University of Agricultural Sciences, Department of Forest Ecology and Management", "@language": "en"}, {"@value": "Sveriges lantbruksuniversitet, Institutionen f\u00f6r skogens ekologi and sk\u00f6tsel", "@language": "sv"}], "@id": "https://ror.org/02yy8x990"}]}, {"@type": "Person", "@id": "https://orcid.org/0000-0002-2161-7371", "sameAs": "https://orcid.org/0000-0002-2161-7371", "name": "Florian Westphal", "email": "florian.westphal@ju.se", "affiliation": [{"@type": "Organization", "name": [{"@value": "J\u00f6nk\u00f6ping University, Department of Computing", "@language": "en"}, {"@value": "H\u00f6gskolan i J\u00f6nk\u00f6ping, Avdelningen f\u00f6r datavetenskap", "@language": "sv"}], "@id": "https://ror.org/03t54am93"}]}], "includedInDataCatalog": {"@type": "DataCatalog", "name": [{"@value": "Swedish National Data Service", "@language": "en"}, {"@value": "Svensk Nationell Datatj\u00e4nst", "@language": "sv"}], "url": "https://snd.se/catalogue/"}, "license": "CC0-1.0", "citation": [{"@type": "CreativeWork", "name": [{"@value": "Paul, S. 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In Ambio (Vol. 52, Issue 1, pp. 182\u2013194). https://doi.org/10.1007/s13280-022-01770-8\n", "@language": "sv"}], "datePublished": "2023", "url": [], "identifier": [{"@type": "PropertyValue", "unitCode": "URN", "value": "urn:nbn:se:uu:diva-494403"}, {"@type": "PropertyValue", "unitCode": "DOI", "value": "https://doi.org/10.1007/s13280-022-01770-8"}], "@id": "https://doi.org/10.1007/s13280-022-01770-8"}, {"@type": "CreativeWork", "name": [{"@value": "Lidberg, W., Paul, S. S., Westphal, F., Richter, K.-F., Lavesson, N., Melniks, R., Ivanovs, J., Ciesielski, M., Leinonen, A., & \u00c5gren, A. M. (2023). Mapping drainage ditches in forested landscapes using deep learning and aerial laser scanning. In Journal of irrigation and drainage engineering (No. 04022051; Vol. 149, Issue 3). https://doi.org/10.1061/jidedh.ireng-9796\n", "@language": "en"}, {"@value": "Lidberg, W., Paul, S. 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