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

arXiv: https://arxiv.org/abs/2008.10041 · HTML: https://mlsanity.com/paper/2008.10041/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [SG-BEV: Satellite-Guided BEV Fusion for Cross-View Semantic Segmentation](https://mlsanity.com/paper/2404.02638/) (2024)
- [Deep Learning for Rheumatoid Arthritis: Joint Detection and Damage Scoring in X-rays](https://mlsanity.com/paper/2104.13915/) (2021)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [Extracting Built Environment Features for Planning Research with Computer Vision: A Review and Discussion of State-of-the-Art Approaches](https://mlsanity.com/paper/2201.12693/) (2022)

## Abstract (arXiv)

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.