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Inventorying urban areas with Very High Resolution Satellite Images

Publication Date
  • Very High Resolution Image
  • Urban Remote Sensing
  • Physical
  • Chemical
  • Mathematical & Earth Sciences :: Earth Sciences & Physical Geography [G02]
  • Physique
  • Chimie
  • Mathématiques & Sciences De La Terre :: Sciences De La Terre & Géographie Physique [G02]
  • Political Science


Prior to the commercial availability of Very High Resolution (VHR) satellite imagery, the applicability of Earth Observation data in the urban planning sector was very limited. The spatial resolution of the imagery, supplied by platforms like Landsat TM and SPOT HRV, was too coarse to be of real practical use to urban planners and their applications. Satellite images of urban or sub-urban areas are characterized by large radiometric variations due to the small size and the diversity of the objects. This in turn causes a radiometric contamination between neighbouring pixels which renders object recognition nearly impossible. Satellite images with a higher resolution might alleviate this problem. The dawn of the VHR era was thus anticipated with great aspiration by urban remote sensing researchers. In the framework of a DWTC/OSTC Telsat 4 pilot project we proposed a methodology to employ IKONOS-21 imagery to develop an inventory of built-up, and un-built areas in Belgium’s Flemish region. Such an inventory can be of use to regional planning agencies that are responsible for the implementation of the government’s planning policies. In Flanders, AROHM (Administration of Spatial Planning, Housing, Monuments and landscapes) records, monitors, and evaluates the built-up areas. To do this, they need an extensive data input from the communities, which requires a lot of time and effort. A reliable and swift technique, based on earth observation data, and applicable for each residential area in Flanders, would be of great value to them. Not only would it allow them to make swift assessments more frequently, they could also double-check incoming data from the communities. The aforementioned project consisted of three parts: the visual interpretation of two study areas (Hasselt and Ghent), the automatic classification of these areas using both Maximum Likelihood and Neural Network classifiers, and the development of GIS procedures to transform the classified images into thematic maps like, for instance, a map of building densities.

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