A Generic Framework for Library-based Mapping
of Urban Areas
What is it about?
Cities are growing faster than ever, and so does the amount of earth observation imagery covering urban areas. We are reaching a point where the sheer extent of image databases exceeds our ability to map them into useful information.
Much research has been dedicated to making more advanced mapping applications, fueled by recent developments in remote sensing and computational sciences. Most applications rely on one or more numerical models that transform image data in desired thematic information.
Building performant models requires high-quality training data (also called reference or ground truth data). Acquiring this data unfortunately takes up a lot of time and effort, in part due to the absence of a standardized approach. Less attention has been paid so far to how we can generate training data in a smarter way.
GENLIB is a research project that addresses this issue. We essentially want to help make mapping of urban environments with optical remote sensing data more efficient and accessible, including for non-expert users. Particular focus is put on automation of training data acquisition, management and optimization.
At the core of this project lies the concept of a spectral library ...
