  Urban planning applications (energy audits, investment, etc.) require an
understanding of built infrastructure and its environment, i.e., both
low-level, physical features (amount of vegetation, building area and geometry
etc.), as well as higher-level concepts such as land use classes (which encode
expert understanding of socio-economic end uses). This kind of data is
expensive and labor-intensive to obtain, which limits its availability
(particularly in developing countries). We analyze patterns in land use in
urban neighborhoods using large-scale satellite imagery data (which is
available worldwide from third-party providers) and state-of-the-art computer
vision techniques based on deep convolutional neural networks. For supervision,
given the limited availability of standard benchmarks for remote-sensing data,
we obtain ground truth land use class labels carefully sampled from open-source
surveys, in particular the Urban Atlas land classification dataset of $20$ land
use classes across $~300$ European cities. We use this data to train and
compare deep architectures which have recently shown good performance on
standard computer vision tasks (image classification and segmentation),
including on geospatial data. Furthermore, we show that the deep
representations extracted from satellite imagery of urban environments can be
used to compare neighborhoods across several cities. We make our dataset
available for other machine learning researchers to use for remote-sensing
applications.
