  We address six different classification tasks related to fine-grained
building attributes: construction type, number of floors, pitch and geometry of
the roof, facade material, and occupancy class. Tackling such a remote building
analysis problem became possible only recently due to growing large-scale
datasets of urban scenes. To this end, we introduce a new benchmarking dataset,
consisting of 49426 images (top-view and street-view) of 9674 buildings. These
photos are further assembled, together with the geometric metadata. The dataset
showcases various real-world challenges, such as occlusions, blur, partially
visible objects, and a broad spectrum of buildings. We propose a new projection
pooling layer, creating a unified, top-view representation of the top-view and
the side views in a high-dimensional space. It allows us to utilize the
building and imagery metadata seamlessly. Introducing this layer improves
classification accuracy -- compared to highly tuned baseline models --
indicating its suitability for building analysis.
