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In this paper we address the challenge of land cover classification forsatellite images via Deep Learning (DL). Land Cover aims to detect the physicalcharacteristics of the territory and estimate the percentage of land occupiedby a certain category of entities: vegetation, residential buildings,industrial areas, forest areas, rivers, lakes, etc. DL is a new paradigm forBig Data analytics and in particular for Computer Vision. The application of DLin images classification for land cover purposes has a great potential owing tothe high degree of automation and computing performance. In particular, theinvention of Convolution Neural Networks (CNNs) was a fundament for theadvancements in this field. In [1], the Satellite Task Team of the UN GlobalWorking Group describes the results achieved so far with respect to the use ofearth observation for Official Statistics. However, in that study, CNNs havenot yet been explored for automatic classification of imagery. This workinvestigates the usage of CNNs for the estimation of land cover indicators,providing evidence of the first promising results. In particular, the paperproposes a customized model, called Satellite-Net, able to reach an accuracylevel up to 98% on test sets.

Satellite-Net: Automatic Extraction of Land Cover Indicators from Satellite Imagery by Deep Learning