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Related Experiment Video

Updated: Jun 23, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Urban Pedestrian Routes' Accessibility Assessment Using Geographic Information System Processing and Deep

Tomás E Martínez-Chao¹, Agustín Menéndez-Díaz², Silverio García-Cortés³

¹Department of Civil, Building and Environmental Engineering, University of Naples "Federico II", 80125 Naples, Italy.

Sensors (Basel, Switzerland)

|June 19, 2024

Summary

This summary is machine-generated.

This study introduces a novel method using AI and GIS to assess urban pedestrian route accessibility, prioritizing inclusivity for people with reduced mobility. The findings help improve safe and accessible pedestrian pathways for all users.

Keywords:

deep learning geographic information system (GIS)inclusiveness inertial sensors pedestrian crossing wheelchair-friendly routes

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Published on: February 25, 2013

Area of Science:

Urban Planning
Geographic Information Science
Artificial Intelligence

Background:

European Union prioritizes safe, accessible, and inclusive pedestrian routes.
Assessing pedestrian mobility around public buildings is crucial for people with reduced mobility.
Existing methods may not fully capture the nuances of accessibility for diverse user groups.

Purpose of the Study:

To develop and validate a method for assessing pedestrian mobility and accessibility in urban areas.
To evaluate the inclusivity of pedestrian routes, particularly for individuals with reduced mobility.
To identify optimal accessible routes for wheelchair users between points of interest.

Main Methods:

Utilized artificial intelligence (AI) algorithms, specifically deep learning-based object detection, for identifying pedestrian crossings from satellite or aerial imagery.
Employed Geographic Information System (GIS) techniques to construct network models for accessibility analysis.
Verified route continuity using inertial sensors to ensure horizontal path integrity.

Main Results:

Successfully identified pedestrian crossings and their precise geographical locations using AI.
Created network models to assess wheelchair accessibility and determine the most suitable transit routes.
Validated the accuracy of the developed method through on-site data corroboration.

Conclusions:

The developed AI and GIS-based method effectively assesses pedestrian route accessibility and inclusivity.
This approach provides valuable data for urban planners and authorities responsible for pedestrian infrastructure.
The findings contribute to creating safer and more accessible urban environments for individuals with reduced mobility.