| Title | National building layer for Canada: recommendations for minimum point density for deriving building footprints from LiDAR |
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| Author | Proulx-Bourque, J -S; McGrath, H ; Bergeron, D; Fortin, C |
| Source | Program, 41st Canadian Symposium on Remote Sensing/Programme, 41e Symposium canadien de télédétection; 2020 p. 96  Open Access |
| Links | Online - En ligne (complete
volume - volume complet, PDF, 17.5 MB)
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| Image |  |
| Year | 2020 |
| Alt Series | Natural Resources Canada, Contribution Series 20200562 |
| Publisher | Canadian Remote Sensing Society |
| Meeting | 41st Canadian Symposium on Remote Sensing; July 13-16, 2020 |
| Document | book |
| Lang. | English |
| Media | on-line; digital |
| File format | pdf |
| Province | Canada; British Columbia; Alberta; Saskatchewan; Manitoba; Ontario; Quebec; New Brunswick; Nova Scotia; Prince Edward Island; Newfoundland and Labrador; Northwest Territories; Yukon; Nunavut;
Canada |
| NTS | 1; 2; 3; 10; 11; 12; 13; 14; 15; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 52; 53; 54; 55; 56; 57; 58; 59; 62; 63; 64; 65;
66; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 82; 83; 84; 85; 86; 87; 88; 89; 92; 93; 94; 95; 96; 97; 98; 99; 102; 103; 104; 105; 106; 107; 114O; 114P; 115; 116; 117; 120; 340; 560 |
| Subjects | Science and Technology; Information and Communications; Government and Politics; digital terrain modelling; models; Geographic data; Buildings; Federal government; Open data; Open government |
| Program | Geobase 2.0 High Resolution Data
Exploitation |
| Released | 2020 07 10 |
| Abstract | LiDAR data is increasingly being collected across Canada and the globe. The point cloud data is used to develop a variety of data layers including, digital terrain and surface models, vegetation,
utility lines, bridges, and buildings, among others. This paper describes an initiative by the Canadian federal government to derive building footprints from LiDAR data in order to generate a national data layer for Canada's Open Data portal. These
LiDAR datasets were acquired since 2009 by a variety of vendors without uniform specifications. Two criteria were tested to evaluate the ability to generate a representative building footprint: minimum point density and the effect of
vendor-classified vs re-classified (classified using open source tools) point data. 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, while a density of at least 8 pts/m² is required to meet this confidence level for re-classified data (automatic classification using open-source tools). |
| Summary | (Plain Language Summary, not published)
Development of recommendations for minimum data requirements in order to successfully create building footprints from LiDAR data. (In order to utilize
existing collection, back to 2009, of LiDAR data). |
| GEOSCAN ID | 327792 |
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