# Determining Accessible Sidewalk Width by Extracting Obstacle Information from Point Clouds

arXiv: https://arxiv.org/abs/2211.04108 · HTML: https://mlsanity.com/paper/2211.04108/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Learning for 3D Point Clouds: A Survey](https://mlsanity.com/paper/1912.12033/) (2019)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Automatic labelling of urban point clouds using data fusion](https://mlsanity.com/paper/2108.13757/) (2021)
- [Sidewalk Measurements from Satellite Images: Preliminary Findings](https://mlsanity.com/paper/2112.06120/) (2021)

## Built on by (most cited first, 1 of 1)

- [Preference Guided Iterated Pareto Referent Optimisation for Accessible Route Planning](https://mlsanity.com/paper/2604.00795/) (2026)

## Abstract (arXiv)

Obstacles on the sidewalk often block the path, limiting passage and resulting in frustration and wasted time, especially for citizens and visitors who use assistive devices (wheelchairs, walkers, strollers, canes, etc). To enable equal participation and use of the city, all citizens should be able to perform and complete their daily activities in a similar amount of time and effort. Therefore, we aim to offer accessibility information regarding sidewalks, so that citizens can better plan their routes, and to help city officials identify the location of bottlenecks and act on them. In this paper we propose a novel pipeline to estimate obstacle-free sidewalk widths based on 3D point cloud data of the city of Amsterdam, as the first step to offer a more complete set of information regarding sidewalk accessibility.