This work aims to assess the risks of Covid-19 disease spread in diverse daily-life situations (referred to as scenarios) involving crowds of unmasked pedestrians, mostly outdoors. More concretely, we develop a method to infer the global number of new infections from patchy observations of pedestrians. The method relies on ad hoc spatially resolved models for disease transmission via virus-laden respiratory droplets, which are fit to existing exposure studies about Covid-19. The approach is applied to the detailed field data about pedestrian trajectories and orientations that we acquired during the pandemic. This allows us to rank the investigated scenarios by the transmission risks that they present; importantly, the obtained hierarchy of risks is conserved across all our transmission models (except the most pessimistic ones): Street cafes present the largest average rate of new infections caused by an attendant, followed by busy outdoor markets, and then metro and train stations, whereas the risks incurred while walking on fairly busy streets (average density around 0.1 person/m^2) are comparatively quite low. Models that assume isotropic transmission of the virus fail to reproduce these results. In scenarios with a moving crowd, we find that density is the main factor influencing the estimated infection rate. Finally, our study explores the efficiency of street and venue redesigns in mitigating the viral spread: While the benefits of enforcing one-way foot traffic in (wide) walkways are unclear, changing the geometry of queues substantially affects disease transmission risks.