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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12128/8801
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dc.contributor.authorBlachnik, Marcin-
dc.contributor.authorSołtysiak, Marek-
dc.contributor.authorDąbrowska, Dominika-
dc.date.accessioned2019-04-11T09:43:51Z-
dc.date.available2019-04-11T09:43:51Z-
dc.date.issued2019-
dc.identifier.citationInternational Journal of Geo-Information, Vol. 8, iss. 3 (2019), art. no. 123pl_PL
dc.identifier.issn2220-9964-
dc.identifier.urihttp://hdl.handle.net/20.500.12128/8801-
dc.description.abstractThe construction of transport infrastructure is often preceded by an environmental impact assessment procedure, which should identify amphibian breeding sites and migration routes. However, the assessment is very difficult to conduct because of the large number of habitats spread out over a vast expanse, and the limited amount of time available for fieldwork. We propose utilizing local environmental variables that can be gathered remotely using only GIS systems and satellite images together with machine learning methods. In this article, we introduce six new and easily extractable types of environmental features. Most of the features we propose can be easily obtained from satellite imagery and spatial development plans. The proposed feature space was evaluated using four machine learning algorithms, namely: a C4.5 decision tree, AdaBoost, random forest and gradient-boosted trees. The obtained results indicated that the proposed feature space facilitated prediction and was comparable to other solutions. Moreover, three of the new proposed features are ranked most important; these are the three dominant properties of the surroundings of water reservoirs. One of the new features is the percentage access from the edges of the reservoir to open areas, but it affects only a few species. Furthermore, our research confirmed that the gradient-boosted trees were the best method for the analyzed dataset.pl_PL
dc.language.isoenpl_PL
dc.rightsUznanie autorstwa 3.0 Polska*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/pl/*
dc.subjectamphibianspl_PL
dc.subjectGISpl_PL
dc.subjectwater reservoirspl_PL
dc.subjectmachine learningpl_PL
dc.titlePredicting Presence of Amphibian Species Using Features Obtained from GIS and Satellite Imagespl_PL
dc.typeinfo:eu-repo/semantics/articlepl_PL
dc.identifier.doi10.3390/ijgi8030123-
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