Background: Childrens' outdoor active play is an important part of their development. Play behaviour can be predicted by a variety of physical and social environmental features. Some of these features are difficult to measure with traditional data sources. Methods: This study investigated the viability of a machine learning method using Google Street View images for measurement of these environmental features. Models to measure natural features, pedestrian traffic, vehicle traffic, bicycle traffic, traffic signals, and sidewalks were developed in one city and tested in another. Results: The models performed well for features that are time invariant, but poorly for features that change over time, especially when tested outside of the context where they were initially trained. Conclusion: This method provides a potential automated data source for the development of prediction models for a variety of physical and social environment features using publicly accessible street view images. Highlights: This study describes an automated method for selecting StreetView images in a given area and extracting from them measures of the built and social environment. Measures obtained in this way are benchmarked against publicly available ground truth measures. The effectiveness of this method varies depending on the nature of the variable being measured. [ABSTRACT FROM AUTHOR]