Developing a CNN for automated detection of Carolina bays from publicly available LiDAR data

For over a century, the enigmatic Carolina bays have captivated geologists and spurred contentious debate on their origin. These circular to ovate and shallow (median diameter of 222 m, median depth of 2.17 m, median area of 26,249 m²) depressions span the Atlantic Coastal Plain (ACP) from northern Florida to southern New Jersey, with total counts ranging between 10,000 and 500,000. Using 1 meter gridded, 1.7 km by 1.7 km LiDAR digital elevation models (DEMs) of Delaware as training images, a convolutional neural network (CNN) was trained to detect Carolina bays. With such a large population size and with such uncertainty around the actual population size, mapping the Carolina bays is a problem that requires an automated detection scheme. Manual detection of bays from LiDAR across the entire Atlantic Coastal Plain would be extremely time intensive and prone to human annotation errors. Using Faster R-CNN within the TensorFlow Python library, a network was trained on 978 LiDAR images for 24 hours (42,450 iterations) on an Intel Core i7-4790K CPU at 4.00 GHz. This network automatically detects bays from LiDAR images with a bounding box and a confidence level. These bounding boxes can then be used to subset and then analyze regions of the DEM for statistics on the bays' three-dimensional shape. Extending this algorithm to DEMs from other areas of the ACP will provide a better understanding of the bays' geographic distribution as well as any differences in morphology between different geographic regions. This method for detecting geomorphic features is a highly efficient process that will provide better means for mapping various types of abundant geomorphic features in the future.