Transportation agencies expend significant resources to inspect critical infrastructure such as roadways, railways, and
pipelines. Regular inspections identify important defects and generate data to forecast maintenance needs. However, cost
and practical limitations prevent the scaling of current inspection methods beyond relatively small portions of the network.
Consequently, existing approaches fail to discover many high-risk defect formations. Remote sensing techniques offer the
potential for more rapid and extensive non-destructive evaluations of the multimodal transportation infrastructure.
However, optical occlusions and limitations in the spatial resolution of typical airborne and space-borne platforms limit
their applicability. This research proposes hyperspectral image classification to isolate transportation infrastructure targets
for high-resolution photogrammetric analysis. A plenoptic swarm of unmanned aircraft systems will capture images with
centimeter-scale spatial resolution, large swaths, and polarization diversity. The light field solution will incorporate
structure-from-motion techniques to reconstruct three-dimensional details of the isolated targets from sequences of two-dimensional
images. A comparative analysis of existing low-power wireless communications standards suggests an
application dependent tradeoff in selecting the best-suited link to coordinate swarming operations. This study further
produced a taxonomy of specific roadway and railway defects, distress symptoms, and other anomalies that the proposed
plenoptic swarm sensing system would identify and characterize to estimate risk levels.