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

Holistic Multi-View Building Analysis in the Wild with Projection Pooling