Abstract
Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Especially the dense buildings and steeply sloped terrain cause difficulties in identifying elevated objects. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. It compares the utility of pixel-based and segment-based features obtained from an orthomosaic and DSM with point-based and segment-based features extracted from the point cloud to classify an unplanned settlement in Kigali, Rwanda. Findings show that the integration of 2D and 3D features leads to higher classification accuracies.
Highlights
Informal settlements are a growing phenomenon in many developing countries and the effort to promote the standard of living in these areas will be a key challenge for the urban planners of many cities in the 21st century (Barry and Rüther, 2005)
The objective of this paper is to integrate the different information sources (i.e. Unmanned Aerial Vehicles (UAVs) point cloud, Digital Surface Model (DSM), and orthomosaic) and to analyse which 2D, 2.5D, and 3D feature sets are most useful for classifying informal settlements, a setting which challenges the boundaries of existing building detection algorithms
A Correlation-Based Feature Selector (CFS) applied to the 2D radiometric and textural and 2.5D topographical features (CFS_all2D) achieves the highest accuracy (87.7%) in the 5-class problem for all pixelbased methods, though it is still achieves a lower accuracy than the RD feature set in the 10-class problem
Summary
Informal settlements are a growing phenomenon in many developing countries and the effort to promote the standard of living in these areas will be a key challenge for the urban planners of many cities in the 21st century (Barry and Rüther, 2005). The identification of buildings gives an indication of the population in the area, classifying terrain identifies footpaths for accessibility and utility planning or free space for the location of infrastructure Such basic information is often lacking at the outset of upgrading projects (Pugalis et al, 2014), hindering the amelioration of the impoverished conditions in these areas. As slums are often characterized by high building densities, small irregular buildings, and narrow footpaths, the spatial resolution provided by sub-meter satellite imagery is usually not sufficient (Kuffer et al, 2014) To this end, Unmanned Aerial Vehicles (UAVs) are useful as they can acquire imagery in a very flexible manner, and provide a cheap alternative to manned aerial surveys in order to generate orthomosaics with sub-decimeter resolution. The question is: how to optimally integrate features from these datasets and assess their relative importance in order to accurately classify informal settlements to support upgrading projects?
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