Automatic Segmentation of Sinkholes Using a Convolutional Neural Network

Muhammad Usman Rafique, Junfeng Zhu, Nathan Jacobs

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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%.

Original languageEnglish
Article numbere2021EA002195
JournalEarth and Space Science
Issue number2
StatePublished - Feb 2022

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No (IIS‐1553116). The second author is supported by the National Science Foundation Grant No (EAR‐1933779). The authors thank Nicole Wong and Aram Ansary Ogholbake for testing the code. We appreciate Dr. William Odom and Dr. Daniel Buscombe for their instructive reviews, which have greatly improved the manuscript.

Publisher Copyright:
© 2022 The Authors. Earth and Space Science published by Wiley Periodicals LLC on behalf of American Geophysical Union.


  • DEM
  • LiDAR
  • U-Net
  • aerial image
  • image segmentation
  • sinkhole

ASJC Scopus subject areas

  • Environmental Science (miscellaneous)
  • Earth and Planetary Sciences (all)


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