This presentation, “Detection of Above Ground Storage Tanks Using Faster R-CNN”, was created by Celine Robinson (Ph.D. student in civil and environmental engineering, Duke University) as part of the Duke University Energy Data Analytics Ph.D. Student Fellowship Program.

Abstract: “Energy systems are essential for society's functioning and economy; however, they are susceptible to natural hazard-induced failures. Extreme hydrological events can cause above ground storage tank (ASTs) floatation, bucking, and sliding failures, resulting in chemical releases that can migrate offsite posing threats to surrounding communities. Unfortunately, infrastructure fragility assessments, which required AST location, type, and volume, have been constrained by limited, incomplete, or inaccessible data. The increasing abundance of high-resolution overhead imagery collected through the National Agriculture Imagery Program (NAIP) may provide a valuable information source. I propose to utilize deep learning methods to create multi-class object detection models and new above ground storage tank datasets for use in natural hazard risk assessment.”

Support for this work was provided by the Alfred P. Sloan Foundation Grant G-2020-13922 through the Duke University Energy Data Analytics Ph.D. Student Fellowship.

Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers, or staff.

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