Quantifying urban land cover by means of machine learning and imaging spectrometer data at multiple spatial scales

The global dimension of urbanization constitutes a great environmental challenge for the 21st century. Remote sensing helps to better understand this dynamic process and its ecological implications. This work demonstrates the use of imaging spectrometer data and machine learning to quantify urban land cover at multiple spatial scales. Experiments consider innovative methodological developments and novel opportunities in urban research that will be created by upcoming hyperspectral satellite missions.