# Combining geolocation and height estimation of objects from street level imagery

arXiv: https://arxiv.org/abs/2305.08232 · HTML: https://mlsanity.com/paper/2305.08232/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 14 May 2023.

## Builds on (most cited first, 9 of 9)

- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale](https://mlsanity.com/paper/1909.04422/) (2019)
- [Automatic Discovery and Geotagging of Objects from Street View Imagery](https://mlsanity.com/paper/1708.08417/) (2017)
- [CBHE: Corner-based Building Height Estimation for Complex Street Scene Images](https://mlsanity.com/paper/1904.11128/) (2019)
- [Deep Markov Random Field for Image Modeling](https://mlsanity.com/paper/1609.02036/) (2016)
- [Combining Maps and Street Level Images for Building Height and Facade Estimation](https://mlsanity.com/paper/1601.07630/) (2016)

## Abstract (arXiv)

We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery, which is considered as a single available input data modality. Our solution is formulated via Markov Random Field optimization with deterministic output. The proposed technique uses image metadata along with coordinates of objects detected in the image plane as found by a custom-trained Convolutional Neural Network. Computing the object height using our methodology, in addition to object geolocation, has negligible effect on the overall computational cost. Accuracy is demonstrated experimentally for water drains and road signs on which we achieve average elevation estimation error lower than 20cm.