An assessment of the utility of LiDAR data in extracting base-year floorspace and a comparison with census-based approach
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Date
2013
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University of New Brunswick
Abstract
Planning models require accurate base-year floorspace data to properly allocate/predict
activities and study the interactions between different land uses. Currently, most planning
models estimate base-year floorspace data according to limited population/employment
data provided by the census and in general their accuracy is unknown. In this study,
building information is extracted from LiDAR data using a free LiDAR classification
software. In addition, through a novel machine learning approach, the extracted buildings
are further enhanced by systematically considering a set of LiDAR features in the
classification process. The accuracy of building information extracted from LiDAR data
and the geographic building footprint layer are then examined and validated through a
field survey. It is found that LiDAR data can provide building height, footprint and,
therefore, floorspace estimates with a good accuracy. Furthermore, two base-year
floorspace estimation methods, one is based on the LiDAR data and the other is based on
census data, are compared for sample zones and distinguished by land-use category. The
results of this study show that the traditional census-based approach may be very
unreliable in estimating base-year floorspace. Comparisons reveal differences as high as
37% for the Residential category. The errors are even higher for the non-residential
categories, with average absolute percent errors ranging from 39% for the Office
floorspace to 190% for the Accommodation and Recreation. Overall, the results obtained
from this study indicate that the traditional census-based approach is very unreliable and
inaccurate for modelers/planners to prepare their base-year floorspace, and therefore
suggest that LiDAR data be used as a powerful add-on for planning models.