Exploration of Open Data in Southeast Asia to Generate 3d Building Models

Abstract

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.

Open government geospatial data on buildings for planning sustainable and resilient cities

“…Building height, elevation, address, year of construction, and number of storeys follow as additional less frequent attributes. The relatively large share (28%) of datasets containing either height or storey information indicates a potential for generating 3D city models using extrusion [22]. A few datasets, e.g.…”

“…In the last decades, other majors actors have started complementing authorities, namely volunteered geoinformation (VGI) initiatives, companies and research institutions [21]. In particular, OpenStreetMap (OSM) -as the key instance of VGI -contains more than half billion buildings around the world, including low-income regions [22,23], and in some cases with high completeness and quality [24,25]. Thanks to its crowdsourcing and collaborative nature, OSM is inherently open and represents an alternative when governments do not release building data [26].…”

Classification of Urban Morphology with Deep Learning: Application on Urban Vitality

“…The geospatial datasets used in this study stem from two main sources. One is OpenStreetMap (OSM), a Volunteered Geographic Information (VGI) platform with global coverage and of high quality in many areas around the world (Barrington-Leigh and Millard-Ball, 2017;Biljecki, 2020), and the others are the authoritative open datasets.…”

Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability

“…These datasets offer resolutions of about 50 cm and higher in major cities in the world, and would be an effective way to assess the scalability and adaptability of our methodology. The building footprints have been extracted from OpenStreetMap, which has a very good level of completeness for the study areas we have selected (Fan et al, 2014;Biljecki, 2020). The building footprints serve a dual purpose: they filter out greenery and solar panels not installed on rooftops, and they are used to integrate the results of our work, enriching the building data.…”