SignificanceWe demonstrate that urban features extracted from street view images through a computer vision model can effectively estimate the hidden neighborhood socioeconomic status, such as travel behaviors, poverty status, health outcomes and behaviors, and crime. Specifically, models using street view features alone can estimate up to 83% of the variance in vehicle miles traveled, 64% in violent crime occurrences, and 68% in the population lacking physical activities. These results outperform models using other commonly adopted data such as points of interest, population, and demographics. With the increasing availability of street view data and readily available computer vision algorithms, this approach could help estimate urban phenomena that concern sustainable development goals at a finer spatial and temporal resolution.