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

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.