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TitleExtraction of building footprints from LiDAR: an assessment of classification and point density requirements
AuthorProulx-Bourque, J -S; McGrath, HORCID logo; Bergeron, D; Fortin, C
SourceAdvances in remote sensing for infrastructure monitoring; by Singhroy, V (ed.); Springer Remote Sensing/Photogrammetry 2020 p. 259-271, 11
LinksOnline - En ligne
Alt SeriesNatural Resources Canada, Contribution Series 20190086
Mediapaper; on-line; digital
File formatpdf
AreaQuebec City
Lat/Long WENS -71.4531 -71.1531 46.9111 46.7400
Subjectsgeophysics; Science and Technology; Society and Culture; geophysical interpretations; Methodology; Buildings; Geographic data
Illustrationslocation maps; satellite imagery; graphs; tables; diagrams
Released2020 12 24
AbstractThere is an increase in the collection and availability of LiDAR data across Canada. While digital terrain and surface models are the most common derivative products, the LiDAR point cloud data can also be used to extract various layers of information, including vegetation, utility lines, bridges, and buildings. This paper describes the current initiative by the Canadian federal government to derive building footprints from LiDAR data in order to generate a building footprint dataset for Canada's Open Data portal and to evaluate the minimum acceptable criteria for successful and accurate building extraction. The results provide guidelines for the minimum density requirements of LiDAR point cloud data for the extraction of building footprints in an urban setting. Two criteria were tested: (i) minimum point density and (ii) the effect of vendor-classified vs re-classified (classified using open-source tools) point data clouds in the building extraction process. Results indicate that vendor-classified point cloud data with a minimum density of 4 pts/m2 is sufficient to accurately extract building footprints with >75% confidence. For re-classified LiDAR a density of at least 8 pts/m2 would be required to meet this confidence level. However, commission errors found in the fully automatic re-classified method are numerous and manual editing or further algorithm refinements are necessary before it could be used in production.
Summary(Plain Language Summary, not published)
Through a comparative analysis of the ability to detect buildings and the quality of the match of the derived footprint shape, recommendations are made for the desired minimal point density for building footprint extraction from airborne LiDAR for urban communities and the capabilities of extracting these footprints from vendor-classified data or un-classififed data that is processed with open source tools.

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