Abstract
This study uses very high-resolution Pléiades imagery for the densely built-up central part of the City of Kigali for the year 2015 in order to derive urban morphology data on building footprints, building archetypes and building heights. Aerial images of the study area from 2008–2009 were used in combination with the 2015 dataset to create a change monitoring dataset on a single building basis. A semi-automated approach was chosen which combined an object-based image analysis with an expert-based revision. The result is a geospatial dataset that detects 165,625 buildings for 2008–2009 and 211,458 for 2015. The dataset includes information on the type of changes between the two dates. Analysis of this geospatial dataset can be used for a range of research applications in economics and the social sciences, as well as a range of policy applications in urban planning and municipal finance administration.
Highlights
This study uses very high-resolution Pléiades imagery for the densely built-up central part of the City of Kigali for the year 2015 in order to derive urban morphology data on building footprints, building archetypes and building heights
Object-based image analysis (OBIA) and expert-based post-classification were applied to derive building footprints and building heights and to assign every building to one of nine building archetypes
Subsequent to the data collection, the measured GPS positions were assigned to the respective reference buildings identified in the remote sensing images by the surveyors, in order to correct for GPS measurement variances
Summary
Urban agglomerations in emerging countries in the Global South are experiencing rapid changes in their urban extent and morphology, due to the growth of population, rural-urban migration, and rising incomes. Very high-resolution (VHR) multispectral satellite images are one important, cost effective source of regular, updated, data on urban land use and built-up areas [1]. The authors acquired a Pléiades satellite image from August 2015 for the central part of the capital city of Rwanda, Kigali. Object-based image analysis (OBIA) and expert-based post-classification were applied to derive building footprints and building heights and to assign every building to one of nine building archetypes. In a second step, building footprint data from aerial images of the same area in 2008–2009 were analyzed, to identify the change of the building stock in the respective period. Data 2019, 4, 105 has expressed an interest in cost-effectively monitoring and managing the building supply in Kigali. A semi-automatic methodology was applied to a Pleaides satellite image of 2015 to derive building footp2ri.nMtsetahnoddsbuilding archetypes. Unplanned settlements become more prevalent further from the CBD and sprawl into the rural areas of the city
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