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https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-37-2020
Title: | EXPLORATION of OPEN DATA in SOUTHEAST ASIA to GENERATE 3D BUILDING MODELS | Authors: | Biljecki, F. | Issue Date: | 2020 | Publisher: | Copernicus GmbH | Citation: | Biljecki, F. (2020). EXPLORATION of OPEN DATA in SOUTHEAST ASIA to GENERATE 3D BUILDING MODELS. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 6 (4/W1) : 37-44. ScholarBank@NUS Repository. https://doi.org/10.5194/isprs-annals-VI-4-W1-2020-37-2020 | Rights: | Attribution 4.0 International | Abstract: | This article investigates the current status of generating 3D building models across 11 countries in Southeast Asia from publicly available data, primarily volunteered geoinformation (OpenStreetMap). The following countries are analysed: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste, and Vietnam. This cross-country study includes multiple spatial levels of analysis: country, town, and micro-level (smaller neighbourhood). The main finding is that authoritative data to generate 3D building models is almost non-existent while building completeness in OpenStreetMap is highly heterogeneous, yielding location-dependent conclusions. While in general just a fraction of mapped buildings has height information and none of the administrative areas provides sufficient information to generate 3D building models, on a micro-level some areas are fully complete, providing a high potential to generate 3D building models on a precinct scale, which may be useful for certain spatial analyses. Furthermore, some areas have high building completeness, requiring only half of the work necessary for the extrusion: the collection of building height attributes. As a part of this work, a semantic 3D building model of a selected set of buildings in Singapore has been generated and released as open data (CityJSON), and the developed code was open-sourced. © Authors 2020. | Source Title: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences | URI: | https://scholarbank.nus.edu.sg/handle/10635/199711 | ISSN: | 2194-9042 | DOI: | 10.5194/isprs-annals-VI-4-W1-2020-37-2020 | Rights: | Attribution 4.0 International |
Appears in Collections: | Staff Publications Elements |
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