Wind Turbine Detection with Synthetic Overhead Imagery

Automatic object detection in overhead imagery is greatly increasing the pace at which we learn about anthropic activity across diverse fields such as economics, environmental management, and engineering. Properly-trained object detection models save significant amounts of human labor when it comes to finding objects, especially rare objects, in overhead imagery. However, applying such techniques to find rare objects typically requires a large amount of labeled imagery data (typically requiring expensive manual labeling). We generate synthetic imagery to reduce the amount of manually labeled imagery required to train models, particularly for data-constrained applications. This approach takes real, unlabeled overhead imagery and inserts artificial 3D models of objects onto the imagery. To evaluate this technique, we collected a baseline dataset of overhead imagery with wind turbines that have been manually labeled and overhead imagery that does not contain wind turbines. We then add synthetic imagery to some of the unlabeled data to create a synthetic dataset. Our results indicate that adding synthetic imagery in training achieving higher levels of recall for similar levels of precision, outperforming the baseline of only real imagery.