Employing spaceborne multispectral stereo pairs and pedestrian flow modeling to support disaster response activities in urban environments

Pedestrian flow modeling applies geocomputational techniques to understand the patterns of human movement within an environment. A key principle is that the “cost” (time, distance, energy, etc.) of travel along different routes is affected by environmental factors, such as terrain variation or land cover. High-probability travel routes can therefore be estimated by performing a least-cost analysis on base geographic data layers. The primary data input to these methods are 3D structural information related to the Earth topography and the objects upon its surface. However, generating and analyzing such data at high resolution (1 m) has only recently become viable at scale. We present a data fusion approach that combines recent advances in dense stereo reconstruction, in conjunction with multispectral-derived land cover, towards exploring the travel routes of pedestrians in urban environments. Most previous studies focus on rural areas; this analysis provides a novel entry into understanding the flow of pedestrians in more densely populated areas, with applications to humanitarian assistance and disaster response (HADR).