Automatic Segmentation of Sinkholes Using a Convolutional Neural Network

M. Usman Rafique

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%.