{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T13:33:18Z","timestamp":1768829598263,"version":"3.49.0"},"reference-count":111,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,7]],"date-time":"2020-11-07T00:00:00Z","timestamp":1604707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011019","name":"Nemzeti Kutat\u00e1si Fejleszt\u00e9si \u00e9s Innov\u00e1ci\u00f3s Hivatal","doi-asserted-by":"publisher","award":["KH 130427"],"award-info":[{"award-number":["KH 130427"]}],"id":[{"id":"10.13039\/501100011019","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Floodplains are valuable scenes of water management and nature conservation. A better understanding of their geomorphological characteristic helps to understand the main processes involved. We performed a classification of floodplain forms in a naturally developed area in Hungary using a Digital Terrain Model (DTM) of aerial laser scanning. We derived 60 geomorphometric variables from the DTM and prepared a geomorphological map of 265 forms (crevasse channels, point bars, swales, levees). Random Forest classification was conducted with Recursive Feature Elimination (RFE) on the objects (mean pixel values by forms) and on the pixels of the variables. We also evaluated the classification probabilities (CP), the spatial uncertainties (SU), and the overfitting in the function of the number of the variables. We found that the object-based method had a better performance (95%) than the pixel-based method (78%). RFE helped to identify the most important 13\u201320 variables, maintaining the high model performance and reducing the overfitting. However, CP and SU were not efficient measures of classification accuracy as they were not in accordance with the class level accuracy metric. Our results help to understand classification results and the specific limits of laser scanned DTMs. This methodology can be useful in geomorphologic mapping.<\/jats:p>","DOI":"10.3390\/rs12213652","type":"journal-article","created":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T19:03:37Z","timestamp":1604862217000},"page":"3652","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Uncertainty and Overfitting in Fluvial Landform Classification Using Laser Scanned Data and Machine Learning: A Comparison of Pixel and Object-Based Approaches"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0668-2474","authenticated-orcid":false,"given":"Zsuzsanna","family":"Csat\u00e1rin\u00e9 Szab\u00f3","sequence":"first","affiliation":[{"name":"Department of Physical Geography and Geoinformatics, University of Debrecen, Doctoral School of Earth Sciences, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4013-8923","authenticated-orcid":false,"given":"Tom\u00e1\u0161","family":"Mikita","sequence":"additional","affiliation":[{"name":"Department of Forest Management and Applied Geoinformatics, Mendel University, Zem\u011bd\u011blsk\u00e1 3, 61300 Brno, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00e1bor","family":"N\u00e9gyesi","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Geoinformatics, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Orsolya Gy\u00f6ngyi","family":"Varga","sequence":"additional","affiliation":[{"name":"Envirosense Hungary Ltd., 4281 L\u00e9tav\u00e9rtes, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5989-5521","authenticated-orcid":false,"given":"P\u00e9ter","family":"Burai","sequence":"additional","affiliation":[{"name":"Remote Sensing Centre, University of Debrecen, B\u00f6sz\u00f6rm\u00e9nyi \u00fat 138, 4023 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"L\u00e1szl\u00f3","family":"Tak\u00e1cs-Szil\u00e1gyi","sequence":"additional","affiliation":[{"name":"Envirosense Hungary Ltd., 4281 L\u00e9tav\u00e9rtes, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2670-7384","authenticated-orcid":false,"given":"Szil\u00e1rd","family":"Szab\u00f3","sequence":"additional","affiliation":[{"name":"Department of Physical Geography and Geoinformatics, University of Debrecen, Egyetem t\u00e9r 1, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,7]]},"reference":[{"key":"ref_1","unstructured":"Pecora, W. 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