Work

Measuring shaded bike lanes for heat stress mitigation with deep learning: A case study in Amsterdam, Netherlands

Year: 2024

Type: article

Abstract: As global warming intensifies and heatwaves become more frequent, thermal comfort has emerged as a crucial factor in creating habitable and equitable urban environments. Cities worldwide are promoting sustainable transportation and reducing vehicle usage by enhancing cycling infrastructures. However, the thermal comfort of cyclists has often been neglected, with most research focusing on pedestrians. This study introduces a framework for evaluating shade levels along the bike lanes by incorporating a ‘Shade Index’ that quantifies street shadiness. Utilizing deep learning techniques, the index analyzes street view images from various bike lane locations to evaluate the contribution of trees and buildings in providing shade. The application of this framework is a shade rating map of the bike lanes in Amsterdam, providing cyclists with alternatives that prioritize coolness over the shortest routes typically suggested by map applications, particularly useful during warmer weather. The study identifies significant variability in shade provision, with central urban areas typically more shaded than outlying regions. The workflow can be adapted to other cities to enhance shade provision for cycling infrastructures.

Source: Urban Climate

DOI: 10.1016/j.uclim.2024.102126

Authors: Biru Cao, Maoran Sun, Ronita Bardhan

Institutions: Massachusetts Institute of Technology, University of Cambridge

Language: English

Topic: Urban Heat Island Mitigation

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $3,340