Data and code for "Translating street view imagery to correct perspectives to enhance bikeability and walkability studies"

Street view imagery (SVI), crucial for evaluating active transportation infrastructure, faces potential biases from its vehicle-based capture method, diverging from pedestrians' and cyclists' perspectives. Existing literature lacks both an examination of these biases and a solution. This study identifies and quantifies these biases by comparing conventional SVI to views from the road shoulder/sidewalk. To mitigate such perspective biases, we introduce a novel framework with Generative Adversarial Networks (GAN)-based image generation models (Pix2Pix and CycleGAN), an image regression model (ResNet-50), and a tabular model (LightGBM). Conducting a case study in Singapore, the research assessed model effectiveness in translating car-centric views to those from pedestrian and cyclist perspectives. Results show significant differences in semantic indicators (e.g., green view index) between road center SVI and road shoulder/sidewalk SVI, with low Pearson's correlation coefficients $r$ (0.35-0.55 for road shoulders and 0.45-0.47 for sidewalks) indicating bias. The framework succeeded in creating realistic images and aligning pixel ratios between perspectives, achieving high correlation coefficients (0.81 for road shoulders and 0.83 for sidewalks), thus reducing bias. This work fills a gap by providing a scalable, model-agnostic approach for producing accurate SVIs for urban planning and sustainability, setting a foundation for improving bikeability and walkability assessments and promoting active transportation.