Holistic Multi-View Building Analysis in the Wild with Projection Pooling
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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 problem of remote building analysis 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 top-view and street-view images of 9674 buildings. These photos are further assembled, together with the geometric metadata. The dataset showcases a variety of 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.
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