Open AccessOPEN STREET MAP DATA AS SOURCE FOR BUILT-UP AND URBAN AREAS ON GLOBAL SCALE
Author(s) -
Thomas Brinkhoff
Publication year - 2016
Publication title -
the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.264
H-Index - 71
eISSN - 1682-1777
pISSN - 1682-1750
DOI - 10.5194/isprsarchives-xli-b4-557-2016
Subject(s) - scale (ratio) , land cover , computer science , cover (algebra) , software , data mining , quality (philosophy) , urban planning , focus (optics) , land use , remote sensing , data science , geography , cartography , civil engineering , engineering , mechanical engineering , philosophy , physics , optics , epistemology , programming language
Many types of applications require information about built-up areas and urban areas. Thus, there is a need for a global, vector-based, up-to-date, and free dataset of high resolution and accuracy. The OpenStreetMap (OSM) dataset fulfills those demands in principle. However, its focus is not land use or land cover. These observations lead to following questions: (1) Which OSM features can be used for computing built-up areas on global scale? (2) How can we derive built-up and urban areas on global scale in sufficient accuracy and performance by using standard software and hardware? (3) Is the quality of the result sufficient on global scale? In this paper, we investigate the first two questions in detail and give some insights into the third question.