# CBHE: Corner-based Building Height Estimation for Complex Street Scene Images

arXiv: https://arxiv.org/abs/1904.11128 · HTML: https://mlsanity.com/paper/1904.11128/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2019.

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

- [Graph Pooling via Coarsened Graph Infomax](https://mlsanity.com/paper/2105.01275/) (2021)
- [WGCN: Graph Convolutional Networks with Weighted Structural Features](https://mlsanity.com/paper/2104.14060/) (2021)
- [Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation](https://mlsanity.com/paper/2307.02574/) (2023)
- [HexCNN: A Framework for Native Hexagonal Convolutional Neural Networks](https://mlsanity.com/paper/2101.10897/) (2021)
- [Combining geolocation and height estimation of objects from street level imagery](https://mlsanity.com/paper/2305.08232/) (2023)

## Abstract (arXiv)

Building height estimation is important in many applications such as 3D city reconstruction, urban planning, and navigation. Recently, a new building height estimation method using street scene images and 2D maps was proposed. This method is more scalable than traditional methods that use high-resolution optical data, LiDAR data, or RADAR data which are expensive to obtain. The method needs to detect building rooflines and then compute building height via the pinhole camera model. We observe that this method has limitations in handling complex street scene images in which buildings overlap with each other and the rooflines are difficult to locate. We propose CBHE, a building height estimation algorithm considering both building corners and rooflines. CBHE first obtains building corner and roofline candidates in street scene images based on building footprints from 2D maps and the camera parameters. Then, we use a deep neural network named BuildingNet to classify and filter corner and roofline candidates. Based on the valid corners and rooflines from BuildingNet, CBHE computes building height via the pinhole camera model. Experimental results show that the proposed BuildingNet yields a higher accuracy on building corner and roofline candidate filtering compared with the state-of-the-art open set classifiers. Meanwhile, CBHE outperforms the baseline algorithm by over 10% in building height estimation accuracy.