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The issue of regular spatial databases updating is partly solved by the abundance of satellite images. It is, though, time consuming, requires qualified human resources, high financial costs and requests efficiency (Bernard, 2007). This article presents a semi-automatic tool for urban detection, to guide the stakeholders and the producers throughout the updating process. The industrial context of the study implies a fast, instantaneous applicative workflow, operational on various landscapes with different sensors; it is thus based on existing algorithms and software resources. The process is generic and adaptable, with a phase of uncorrelation, chaining a Minimum Noise Fraction transformation with a textural analysis, a learning phase, processed from an existing database, and an automatic modelling of the detected objects. The quantification of the results shows the successful recreation of the existing database (90% of its surface) with a 7% rate of potential big omissions. A specific highlight is made on the detection of disappeared buildings, corresponding to 17.5% of the potential important omissions. This process has run in “real” updating operations, on 1.5 and 6 meters resolution Spot6 images, a 15 meters Landsat-8 image and a 1.5 meters resolution Pleiades image. A quantification of its results is also proposed in this study.

Urban Indicator for Database Updating - A Decision Tool to Help Stakeholders and Map Producers