Definition of a reference data set to assess the quality of building information extracted automatically from VHR satellite images

Rapid urbanisation continues to be an issue with one third of the world’s urban population living under poor living conditions in informal settlements or shanty towns. Improving the lives of this population, which is one objective of the United Nations’ Millennium Development Goals, requires knowledge about the areas under concern. This information is currently still collected in intensive field studies. Earth observation data could be an alternative source of information that can support the process of information collection and on longer terms also serves for monitoring the evolution of those areas. Today’s sub-meter resolution satellites provide very detailed information allowing identification of different urban pattern. However, the huge amount of data requires an automatic information extraction to derive relevant information in a fast and consistent way. Reference data is crucial for the assessment of those algorithms but in case of absence of a relevant data set, which is especially the case in developing countries, an alternative database needs to be defined. In this paper we present a robust approach to produce a reference data set with limited field surveys for the city of Harare (Zimbabwe). This data set is defined to serve two objectives: first, quality assessment of the automatically extracted information and second, further analysis to identify built-up pattern. Two reference data sets are defined using systematic and cluster sampling. The building stock of the city is systematically sampled by visual image interpretation of regular grid points covering the entire image area. Cluster sampling in several stages is performed to define a small sample of buildings that are surveyed in the field. The first stage consists of constructing clusters for the entire city area based on building density and height. From each of these clusters, a sample is randomly selected and in each a sample of buildings is finally randomly selected.

WANIA Annett;  KEMPER Thomas;  EHRLICH Daniele;  SOILLE Pierre;  GALLEGO PINILLA Francisco; 

2013-04-16

Publications Office of the European Union

JRC76341

978-92-79-21379-3 (print),    978-92-79-21380-9,   

1018-5593 (print),    1831-9424 (online),   

https://publications.jrc.ec.europa.eu/repository/handle/JRC76341,   

10.2788/73045 (print),    10.2788/74875 (online),