Analysis of urban footprint

Namen magistrske naloge je analizirati kakovost izdelkov projekta Globalni urbani odtis (Global Urban Footprint) in določiti rast slovenskih mest na podlagi satelitskih posnetkov srednje prostorske ločljivosti. Sloj Globalnega urbanega odtisa je bil po površini primerjan z evidenco dejanske rabe in izdelano masko pozidanosti na podlagi posnetkov Landsat. Za vse maske je bila ovrednotena klasifikacija. Za obdobje med 1984 in 2018 je bila izdelana časovna vrsta posnetkov Landsat. Vrsta prikazuje rast štirih izbranih slovenskih mest. Na podlagi časovne vrste so se razkrila večja območja sprememb zaradi gradenj oz. rušenja stavb. S tem smo želeli odkriti trende in značilnosti razvoja mest v Sloveniji. Maska pozidanosti na podlagi časovnih vrst posnetkov Landsat je bila izdelana v spletni storitvi Google Earth Engine v programskem jeziku Javascript. Pozidano površje je bilo določeno na podlagi analize glavnih komponent, ki se je izkazala za najnatančnejšo med preizkušenimi metodami.The aim of this Master thesis is to analyze the quality of Global Urban Footprint project’s products and to determine the growth of Slovenian cities based on medium spatial resolution satellite images. The Global Urban Footprint layer was compared to the actual usage record by the surface and to the built-up mask based on Landsat footage. All masks have been evaluated with a classification. For the period between 1984 and 2018 the time series of the Landsat footage was created. The latter shows the growth of four selected Slovenian cities. Based on the time series, large areas of changes were revealed, due to the construction or the demolition of buildings. Through this, the intention was to discover trends and characteristics of urban development in Slovenia. The built-up mask based on the time series of Landsat footage was created in the online service Google Earth Engine, in the JavaScript programming language. The built-up area was determined based on the principal component analysis (PCA), which was proven to be the most accurate among the tested methods