Automatic identification of building types based on topographic databases – a comparison of different data sources

Hecht, Robert; Meinel, Gotthard; Buchroithner, Manfred

doi:10.1080/23729333.2015.1055644

Cited by 84 publications

(75 citation statements)

References 27 publications

(28 reference statements)

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“…A possible solution could be the introduction of a building classification approach [35,43]. A semantic enrichment of the data would help to eliminate the right buildings.…”

Section: Discussion Of the Approach As A Wholementioning

confidence: 99%

“…The small and irrelevant buildings can be identified due to their small size. Hecht [35] (p. 156) verified that detached buildings with a footprint size of 56 square meters and attached buildings with a footprint size of 39 square meters have a probability of 99% that they are not residential buildings. Therefore, all buildings with a footprint smaller than 39 square meters are omitted.…”

Section: Delineate Built-up Area Using Building Polygons and Road Netmentioning

confidence: 99%

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A Supervised Approach to Delineate Built-Up Areas for Monitoring and Analysis of Settlements

Harig

Burghardt

Hecht

2016

IJGI

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Abstract:Monitoring urban growth and measuring urban sprawl is essential for improving urban planning and development. In this paper, we introduce a supervised approach for the delineation of urban areas using commonly available topographic data and commercial GIS software. The method uses a supervised parameter optimization approach along with buffer-based quality measuring method. The approach was developed, tested and evaluated in terms of possible usage in monitoring built-up areas in spatial science at a very fine-grained level. Results show that built-up area boundaries can be delineated automatically with higher quality compared to the settlement boundaries actually used. The approach has been applied to 166 settlement bodies in Germany. The study shows a very efficient way of extracting settlement boundaries from topographic data and maps and contributes to the quantification and monitoring of urban sprawl. Moreover, the findings from this study can potentially guide policy makers and urban planners from other countries.

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“…A possible solution could be the introduction of a building classification approach [35,43]. A semantic enrichment of the data would help to eliminate the right buildings.…”

Section: Discussion Of the Approach As A Wholementioning

confidence: 99%

Section: Delineate Built-up Area Using Building Polygons and Road Netmentioning

confidence: 99%

A Supervised Approach to Delineate Built-Up Areas for Monitoring and Analysis of Settlements

Harig

Burghardt

Hecht

2016

IJGI

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“…height) gives a hint at the building type, thus we investigate in this paper whether age could also be successfully estimated in a similar fashion. A similar work was carried out by Hecht et al (2015) using a Random Forest classifier and by integrating multiple data sources. In a regression problem, Biljecki et al (2017) use several building attributes (e.g.…”

Section: Related Workmentioning

confidence: 99%

Estimating Building Age With 3d Gis

Biljecki

Sindram

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Building datasets (e.g. footprints in OpenStreetMap and 3D city models) are becoming increasingly available worldwide. However, the thematic (attribute) aspect is not always given attention, as many of such datasets are lacking in completeness of attributes. A prominent attribute of buildings is the year of construction, which is useful for some applications, but its availability may be scarce. This paper explores the potential of estimating the year of construction (or age) of buildings from other attributes using random forest regression. The developed method has a two-fold benefit: enriching datasets and quality control (verification of existing attributes). Experiments are carried out on a semantically rich LOD1 dataset of Rotterdam in the Netherlands using 9 attributes. The results are mixed: the accuracy in the estimation of building age depends on the available information used in the regression model. In the best scenario we have achieved predictions with an RMSE of 11 years, but in more realistic situations with limited knowledge about buildings the error is much larger (RMSE = 26 years). Hence the main conclusion of the paper is that inferring building age with 3D city models is possible to a certain extent because it reveals the approximate period of construction, but precise estimations remain a difficult task.

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“…According to their basic orientation, such methods can be divided into knowledge-based ("top-down") and data-driven ("bottom-up") approaches (cf. [34]). Data-driven approaches make use of processes of machine learning to automatically train a classifier on a training data sample.…”

Section: Semantic Enrichment Of Building Footprintsmentioning

confidence: 99%

A Workflow for Automatic Quantification of Structure and Dynamic of the German Building Stock Using Official Spatial Data

Hartmann

Meinel

Hecht

et al. 2016

IJGI

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Knowledge of the German building stock is largely based on census data and annual construction statistics. Despite the wide range of statistical data, they are constrained in terms of temporal, thematic and spatial resolution, and hence do not satisfy all requirements of spatial planning and research. In this paper, we describe a new workflow for data integration that allows the quantification of the structure and dynamic of national building stocks by analyzing authoritative geodata. The proposed workflow has been developed, tested and demonstrated exemplarily for the whole country of Germany. We use nationwide and commonly available authoritative geodata products such as building footprint and address data derived from the real estate cadaster and land use information from the digital landscape model. The processing steps are (1) data preprocessing; (2) the calculation of building attributes; (3) semantic enrichment of the building using a classification tree; (4) the intersection with spatial units; and finally (5) the quantification and cartographic visualization of the building structure and dynamic. Applying the workflow to German authoritative geodata, it was possible to describe the entire building stock by 48 million polygons at different scale levels. Approximately one third of the total building stock are outbuildings. The methodological approach reveals that 62% of residential buildings are detached, 80% semi-detached and 20% terraced houses. The approach and the novel database will be very valuable for urban and energy modeling, material flow analysis, risk assessment and facility management.

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Automatic identification of building types based on topographic databases – a comparison of different data sources

Cited by 84 publications

References 27 publications

A Supervised Approach to Delineate Built-Up Areas for Monitoring and Analysis of Settlements

A Supervised Approach to Delineate Built-Up Areas for Monitoring and Analysis of Settlements

Estimating Building Age With 3d Gis

A Workflow for Automatic Quantification of Structure and Dynamic of the German Building Stock Using Official Spatial Data

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