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Presenter: Øivind Due Trier, Norwegian Computing Center, Section for Earth Observation, Norway
Urban maps in Norway are currently updated using manual photo interpretation on stereo aerial imagery. However, there is often a substantial delay after completion of construction work until new buildings, roads, etc. appear in updated versions of the urban maps.
Automated pixel-based urban land cover classification from multispectral aerial images of very high resolution has proven difficult since the same spectral values may occur within several land cover types. Airborne hyperspectral data may provide better discriminative power. However, there is still the problem that the same types of material may exist within different land cover types, such as buildings, roads, parks, gardens, etc.
With the rapid development in deep neural network methods and computer processing resources, it should be possible to develop methods that could automate at least some of the urban map revision tasks. Detection and mapping of new and/or changed buildings is one such task that is important in Norway.
This research is part of a project on machine learning in map revision, financed by: Regional Research Fund Viken, Bærum municipality, TerraTec AS and Geovekst.
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