AbstractUrban built environments often include many negative stimuli (e.g., unleashed dogs, dead animals, litter, graffiti, abandoned vehicles) that are linked with stress symptomatology among urban populations. Biosignals (e.g., electrodermal activity, gait patterns, and blood volume pulse) can help assess pedestrian distress levels induced by negative environmental stimuli by overcoming the measurement limitations of traditional self-reporting methods and field observations. Despite their potential, biosignals from naturalistic outdoor environments are often contaminated by uncontrollable extraneous factors (e.g., movement artifacts, physiological reactivity due to unintended stimuli, and individual variability). Thus, more quantitative evidence and novel methodological approaches are required to accurately capture pedestrian environmental distress resulting from negative environmental stimuli. In this context, we investigate the interplay between pedestrians’ biosignal data and image-based data (built environment feature information and perceptual distress levels identified from images) in a machine learning model. Results from the statistical model estimated with the biosignal data demonstrated significant physiological responses to the negative environmental stimuli. The use of the features from image-based data increased the prediction accuracy of the computational model. This method can be applied to geospatial intelligence, further advancing built environmental assessments and evidence-based approaches to promote walking and walkable communities.