{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:32:51Z","timestamp":1740101571349,"version":"3.37.3"},"posted":{"date-parts":[[2024,9,12]]},"group-title":"Environmental and Earth Sciences","reference-count":0,"publisher":"MDPI AG","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,9,11]]},"abstract":"<jats:p>ECOCLIMAP-SG+ is a new 60~m land use land cover dataset, which covers a continental domain, and represents the 33 labels of the original ECOCLIMAP-SG dataset. ECOCLIMAP-SG is used in HARMONIE-AROME, the numerical weather prediction model used operationally by Met &amp;Eacute;ireann and other national meteorological services.  ECOCLIMAP-SG+ was created using an agreement-based method to combine information from many maps to overcome variations in semantic and geographical coverage, resolutions, formats, accuracies, and representative periods. In addition to ECOCLIMAP-SG+, the process generates an agreement score map, which estimates the uncertainty of the land cover labels in ECOCLIMAP-SG+ at each location in the domain. This work presents the first evaluation of ECOCLIMAP-SG and ECOCLIMAP-SG+ against the following trusted land cover maps: LUCAS 2022, the Irish National Land Cover 2018 dataset, and an Icelandic version of ECOCLIMAP-SG. Using a set of primary labels, ECOCLIMAP-SG+ outperforms ECOCLIMAP-SG regarding the F1-score against LUCAS 2022 over Europe and the Irish national land cover 2018 dataset. Similarly, it outperforms ECOCLIMAP-SG against the Icelandic version of ECOCLIMAP-SG for most of the represented secondary labels.  The score map shows that the quality ECOCLIMAP-SG+ is hetereogeneous. It could be improved once new maps once they become available but we do not control when they will be available. Therefore, the second-part of this publication series aims at improving the map using machine learning.<\/jats:p>","DOI":"10.20944\/preprints202409.0953.v1","type":"posted-content","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T01:17:23Z","timestamp":1726190243000},"source":"Crossref","is-referenced-by-count":3,"title":["High-Resolution Land Use Land Cover Dataset for Meteorological Modelling \u2013 Part 1: ECOCLIMAP-SG+ an Agreement-Based Dataset"],"prefix":"10.20944","author":[{"given":"Geoffrey","family":"Bessardon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5788-8977","authenticated-orcid":false,"given":"Thomas","family":"Rieutord","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3795-4321","authenticated-orcid":false,"given":"Emily","family":"Gleeson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandro","family":"Oswald","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bolli","family":"Palmason","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","container-title":[],"original-title":[],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T03:16:19Z","timestamp":1730949379000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.preprints.org\/manuscript\/202409.0953\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,12]]},"references-count":0,"URL":"https:\/\/doi.org\/10.20944\/preprints202409.0953.v1","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.3390\/land13111811","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2024,9,12]]},"subtype":"preprint"}}