New paper in IJGIS!

With Shivangi Srivastava, John Vargas and Devis Tuia, we recently published an article on land-use classification using ground based pictures (e.g. Google Street View images):

Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data

Shivangi Srivastava, John Vargas, Sylvain Lobry, Devis Tuia

in International Journal of Geographical Information Science (IJGIS)

Good news: it is published in open access!

GSV Images

Ground-based pictures (both inside and outside) from Google Street View.

This work assigns a land-use class (e.g. religious place, hotel, post office, …) to buildings, which is an essential task towards better urban land management. In order to do this classification, we use the images from Google Street View pointing to a building as an input to a variable input siamese network (allowing to take into account the information coming from an unknown number of images). This network has been trained using ground truth labels extracted from OpenStreetMap (which are freely available).