Assessing Military Parking: A Deep Learning Approach to Evaluating Standards and Impacts

Current United States Department of Defense (DoD) standards require a minimum amount of parking for each building. This requirement defines how much off-street parking to construct. However, the impact of these requirements remains unclear. This study builds upon the emerging field of overhead imagery analytics by directly tying it to parking on military installations. Specifically, this study leverages a pretrained deep learning car detection model, Car Detection – USA, developed by Esri for use within ArcGIS, and couples it with open-access temporal imagery sourced from Google Earth Pro to assess selected parking lots across Area B, Wright-Patterson Air Force Base, a representative United States military installation, during a 10-year period. This study determined the model is suitable for assessing parking lot usage due to its high level of performance when presented with a test dataset. However, it is poorly suited for such assessments without manual user oversight and validation and the use of high-resolution imagery as it tends to underestimate parking lot usage when presented with a deployment dataset. Additionally, this study determined that parking supply within assessed parking lots exceeded demand across the 10-year assessment period, resulting in unnecessary excess financial burdens in operating and maintenance costs. These results suggest parking policy standards within the DoD may necessitate excessive parking, indicating a broader DoD-wide issue. To this author's knowledge, this research is the first to combine deep learning object detection software with satellite imagery data to study parking policy, parking lot usage, and its economic impacts on a representative DoD installation.