TY - JOUR AU - Sobieraj, Janusz AU - Metelski, Dominik Igor PY - 2022 UR - https://hdl.handle.net/10481/79006 AB - The aim of this study is to extract impervious surfaces and show their spatial distribution, using different machine learning algorithms. For this purpose, geoprocessing and remote sensing techniques were used and three classification methods for... LA - eng PB - MDPI KW - Support Vector Machines (SVM) KW - Maximum likelihood (ML) KW - Random trees (RT) KW - Impervious surfaces KW - Land use and land cover (LULC) KW - Normalised Difference Vegetation Index (NDVI) KW - Multispectral imagery KW - Machine learning KW - Construction industry TI - A Comparison of Different Machine Learning Algorithms in the Classification of Impervious Surfaces: Case Study of the Housing Estate Fort Bema in Warsaw (Poland) DO - 10.3390/buildings12122115 ER -