Buildings in Launceston
Geoscience Australia, Tasmania
Geoscience Australia wanted to be able to use remote sensed LiDAR and imagery data to determine building footprints and potential floor heights for residential and commercial buildings on a broad scale across urban areas.
Such information is useful in undertaking flood studies, for insurance purposes, and in developing an understanding of building characteristics across large areas.
A pilot study was commissioned focusing on 10,000 residential and commercial buildings in Launceston, Tasmania.
The approach taken was to re-classify the LiDAR data provided to accurately identify ground and buildings and then extract key characteristics for all the buildings in the area assisted by imagery.
Classification was performed swathe by swathe due to the presence of LiDAR input data alignment and quality issues. The resulting dataset was then fully aligned using 3D solution fields specifically developed for the project. The imagery data was used as a visual aid tool.
Automated routines were employed to extract building footprints from the refined LiDAR data with subsequent checking against available imagery.
- Re-classification and refinement
- Customised feature detection
- Integrated analysis
LiDAR dataset analysis and pre-processing reduced vertical and horizontal misalignment from in the order of 300 mm to less than 10 mm. This gave the necessary foundation for accurate spatial analytics.
Examination of the results showed that heights of building ridges and eaves across such a large area could be accurately determined with this method.
Using 2 points/m2 LiDAR data gave a reasonably accurate Digital Elevation Model (DEM) and building footprints as well. Higher resolution LiDAR at 4 points/m2 further improved the quality of building footprint extraction, as did the integrated use of imagery if well-aligned with the LiDAR data.
Enhanced data value
Outcomes enabled by advanced pre-processing
Dramatically faster than traditional methods
Thousands of buildings in one integrated analysis