Abstract
Abstract. Urban density is an important factor for several fields, e.g. urban design, planning and land management. Modern remote sensors deliver ample information for the estimation of specific urban land classification classes (2D indicators), and the height of urban land classification objects (3D indicators) within an Area of Interest (AOI). In this research, two of these indicators, Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) are numerically and automatically derived from high-resolution airborne RGB orthophotos and LiDAR data. In the pre-processing step the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an improved normalized digital surface model (nDSM) is an upsampled elevation data with considerable improvement regarding region filling and “straightness” of elevation discontinuities. In a following step, a Multilayer Feedforward Neural Network (MFNN) is used to classify all pixels of the AOI to building or non-building categories. For the total surface of the block and the buildings we consider the number of their pixels and the surface of the unit pixel. Comparisons of the automatically derived BCR and FAR indicators with manually derived ones shows the applicability and effectiveness of the methodology proposed.
Highlights
Urban density is an important factor for several fields, e.g. urban design, planning and land management
To upsample the normalized digital surface model (nDSM), we employed a preprocessing technique described in Gyftakis et al, (2014) Based on the implicit assumption that the optical data can provide the necessary information about the significant edges of the scene, we fuse the elevation information with a high resolution orthophoto color image of the same region
The approach is based on a combination of a variant of the mean shift algorithm and a neural network based classification
Summary
Urban density is an important factor for several fields, e.g. urban design, planning and land management. Kubota et al (2008) investigated the relationship between the building density of a residential neighborhood and the average wind speed at pedestrian level. They found that by increasing the building’s coverage ratio, the wind speed decreases. The most commonly used indices for quantifying the building density at land lot scale are the Building Coverage Ratio (BCR) and Floor Area Ratio (FAR). The BCR is defined as the ratio of the building coverage area (i.e. the area of building footprint) to the size of land lot (Eq (1))
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