The ability to retrieve and analyze recent images of critical infrastructure assets is beneficial for regular monitoring, post-disaster assessment, or preparing for a service call. Given a high-quality image of an asset, several recently developed deep learning models can automatically assess the state of the infrastructure. However, obtaining such an image automatically remains an open question. Enterprise imaging initiatives, such as Google Street View, permit the viewing of road-adjacent images, given geographic coordinates. The spatial resolution of such systems is excellent, although the temporal resolution varies from months to years. We have recently forecast the emergence of on-demand imaging using instrumented vehicles that would permit more recent or frequent imaging of locations of interest. However, the challenge remains to retrieve a high-quality image of an asset of interest, free from obstructions and imaging artifacts. We here propose a pipeline to retrieve recent images of an asset given an imaging source, GPS coordinates, and an asset class. Object detection is used to automatically identify the asset of interest and to detect obstructions or imaging artifacts. If necessary, additional images are requested for surrounding locations to provide multiple views of the asset of interest culminating in an image free from artifacts. The pipeline is demonstrated using two critical infrastructure asset classes (utility poles and street lights) and two image sources (Streetview and a repository of dashcam video). Robust performance is observed, resulting in correct asset identification and imaging in 76.5% of cases (up from 54.5%), while requiring an average of 1.47 images per asset to achieve a high-quality image free from obstructions and artifacts. The proposed pipeline will be of interest to disaster response teams, utilities, and other critical infrastructure asset managers.
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