Extraction of building information using geographic object-based image analysis

Fan, Shudi; Liu, Zhenhua; Hu, Yueming

doi:10.1109/eorsa.2016.7552783

Cited by 1 publication

(5 citation statements)

References 13 publications

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“…Previous work [53,54], performed the detection of buildings using a similar GEOBIA approach, showed comparable accuracy to our results (KIA: 0.95 and 0.92). Tiwari et al (2020) developed an object-based algorithm on orthophoto (GSD 0.25 m) to extract all buildings in a small city in Israel with a Kappa index of agreement of 0.95 (UA and PA of the building classes are 99.38% and 98.03%) [53].…”

Section: Discussionsupporting

confidence: 84%

“…The wall-to wall LiDAR ALS point clouds coverage is available for many European countries (e.g., in Denmark, Estonia, Finland, Latvia, Luxembourg, the Netherlands, Poland, Slovenia, Spain, Sweden, Switzerland, UK), but time series analysis is still not always possible due to some gaps in the data from several years. [44,45,54]).…”

Section: Discussionmentioning

confidence: 99%

“…Our methodology is not limited to aerial orthophotos, very high resolution (VHR) satellite imagery is often used for building detection and offers high flexibility when it comes to data available. Fan et al (2016) based on Quickbird-2 image satellites (GSD 0.61 m) extracted information of buildings in an urban area with an overall accuracy of 87% and Kappa value of 0.87 [54]. Warth et al (2020) predicted socio-economic indicators for urban planning using WorldView-1 (GSD 0.5 m), PlanetScope (3.0 m) satellite imagery, and collected ground-truthing data [55].…”

Section: Discussionmentioning

confidence: 99%

“…To obtain the height of the buildings for the archival data, the Urban Atlas Buildings Height layer (2011) was used to compute the volume of the buildings that had been modified and could cause a significant demolition- Figure 18. Volume of buildings per resident in European cities (Source: based on data in publication [44,45,54]).…”

Section: Discussionmentioning

confidence: 99%

“…Volume of buildings per resident in European cities (Source: based on data in publication[44,45,54]). …”

mentioning

confidence: 99%

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Spatiotemporal Changes in 3D Building Density with LiDAR and GEOBIA: A City-Level Analysis

et al. 2020

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Understanding how, where, and when a city is expanding can inform better ways to make our cities more resilient, sustainable, and equitable. This paper explores urban volumetry using the Building 3D Density Index (B3DI) in 2001, 2010, 2019, and quantifies changes in the volume of buildings and urban expansion in Luxembourg City over the last two decades. For this purpose, we use airborne laser scanning (ALS) point cloud (2019) and geographic object-based image analysis (GEOBIA) of aerial orthophotos (2001, 2010) to extract 3D models, footprints of buildings and calculate the volume of individual buildings and B3DI in the frame of a 100 × 100 m grid, at the level of parcels, districts, and city scale. Findings indicate that the B3DI has notably increased in the past 20 years from 0.77 m3/m2 (2001) to 0.9 m3/m2 (2010) to 1.09 m3/m2 (2019). Further, the increase in the volume of buildings between 2001–2019 was +16 million m3. The general trend of changes in the cubic capacity of buildings per resident shows a decrease from 522 m3/resident in 2001, to 460 m3/resident in 2019, which, with the simultaneous appearance of new buildings and fast population growth, represents the dynamic development of the city.

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%