Abstract
Sinkholes are the most abundant surface features in karst areas worldwide. Understanding sinkhole occurrences and characteristics is critical for studying karst aquifers and mitigating sinkhole-related hazards. Most sinkholes appear on the land surface as depressions or cover collapses and are commonly mapped from elevation data, such as digital elevation models (DEMs). Existing methods for identifying sinkholes from DEMs often require two steps: locating surface depressions and separating sinkholes from non-sinkhole depressions. In this study, we explored deep learning to directly identify sinkholes from DEM data and aerial imagery. A key contribution of our study is an evaluation of various ways of integrating these two types of raster data. We used an image segmentation model, U-Net, to locate sinkholes. We trained separate U-Net models based on four input images of elevation data: a DEM image, a slope image, a DEM gradient image, and a DEM-shaded relief image. Three normalization techniques (Global, Gaussian, and Instance) were applied to improve the model performance. Model results suggest that deep learning is a viable method to identify sinkholes directly from the images of elevation data. In particular, DEM gradient data provided the best input for U-net image segmentation models to locate sinkholes. The model using the DEM gradient image with Gaussian normalization achieved the best performance with a sinkhole intersection-over-union (IoU) of 45.38% on the unseen test set. Aerial images, however, were not useful in training deep learning models for sinkholes as the models using an aerial image as input achieved sinkhole IoUs below 3%.
Document Type
Article
Publication Date
2-7-2022
Digital Object Identifier (DOI)
https://doi.org/10.1029/2021EA002195
Repository Citation
Rafique, Muhammad Usman; Zhu, Junfeng; and Jacobs, Nathan, "Automatic Segmentation of Sinkholes Using a Convolutional Neural Network" (2022). Computer Science Faculty Publications. 39.
https://uknowledge.uky.edu/cs_facpub/39
