1,404
Views
3
CrossRef citations to date
0
Altmetric
Research Article

Classification of drainage crossings on high-resolution digital elevation models: A deep learning approach

, , , , &
Article: 2230706 | Received 03 Oct 2022, Accepted 22 Jun 2023, Published online: 03 Jul 2023

References

  • Agarap, A. F. 2018. “Deep Learning Using Rectified Linear Units (Relu).” arXiv preprint arXiv:1803.08375.
  • Aristizabal, F., L. E. Grimley, J. Bales, D. Tijerina, T. Flowers, and E. P. Clark. 2018. “National Water Centers Innovators Program Summer Institute Report.” Consortium of Universities for the Advancement of Hydrologic Science, Inc. Technical Report No 15.
  • Barry-Straume, J., A. Tschannen, D. W. Engels, and E. Fine. 2018. “An Evaluation of Training Size Impact on Validation Accuracy for Optimized Convolutional Neural Networks.” SMU Data Science Review 1 (4): 12.
  • Berrar, D. 2019. “Cross-Validation.” Encyclopedia of Bioinformatics and Computational Biology 1:542–14.
  • Bhadra, S., R. Li, D. Wu, G. Wang, and B. Rekabdar. 2021. “Assessing the Roles of Anthropogenic Drainage Structures on Hydrologic Connectivity Using High-Resolution Digital Elevation Models.” Transactions in GIS 25 (5): 2596–2611. https://doi.org/10.1111/tgis.12832.
  • Brown, J. 2015. “NDVI, the Foundation for Remote Sensing Phenology.” USGS. Accessed November 27, 2018. https://www.usgs.gov/special-topics/remote-sensing-phenology/science/ndvi-foundation-remote-sensing-phenology.
  • Callow, J. N., K. P. Van Niel, and G. S. Boggs. 2007. “How Does Modifying a DEM to Reflect Known Hydrology Affect Subsequent Terrain Analysis?” Journal of Hydrology 332 (1–2): 30–39. https://doi.org/10.1016/j.jhydrol.2006.06.020.
  • Chen, W., X. Zhao, H. Shahabi, A. Shirzadi, K. Khosravi, H. Chai, S. Zhang, et al. 2019. “Spatial Prediction of Landslide Susceptibility by Combining Evidential Belief Function, Logistic Regression and Logistic Model Tree.” Geocarto International 34 (11): 1177–1201. https://doi.org/10.1080/10106049.2019.1588393.
  • Davies, K. W., S. L. Petersen, D. D. Johnson, D. B. Davis, M. D. Madsen, D. L. Zvirzdin, and J. D. Bates. 2010. “Estimating Juniper Cover from National Agriculture Imagery Program (NAIP) Imagery and Evaluating Relationships Between Potential Cover and Environmental Variables.” Rangeland Ecology & Management 63 (6): 630–637. https://doi.org/10.2111/REM-D-09-00129.1.
  • Deng, J., W. Dong, R. Socher, L. J. Li, K. Li, and F. Li. 2009, June. “Imagenet: A Large-Scale Hierarchical Image Database.” In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, https://doi.org/10.1109/CVPR.2009.5206848.
  • Duke, G. D., S. W. Kienzle, D. L. Johnson, and J. M. Byrne. 2003. “Improving Overland Flow Routing by Incorporating Ancillary Road Data into Digital Elevation Models.” Journal of Spatial Hydrology 3 (2). Article 2. Available at. https://scholarsarchive.byu.edu/josh/vol3/iss2/2.
  • Finlayson, G. D., B. Schiele, and J. L. Crowley. 1998, June. Comprehensive Colour Image Normalization. In European conference on computer vision (pp. 475–490). Springer, Berlin, Heidelberg.
  • Gelder, B. K., Z. Zhou, and A. Yu. 2015. “Automation of DEM Cutting for Hydrologic/Hydraulic Modeling.” Tech Transfer Summaries 62 (1): 62–77. https://doi.org/10.1002/aic.15050.
  • Good, S. P., D. Noone, and G. Bowen. 2015. “Hydrologic Connectivity Constrains Partitioning of Global Terrestrial Water Fluxes.” Science: Advanced Materials and Devices 349 (6244): 175–177. https://doi.org/10.1126/science.aaa5931.
  • Google. 2023. Maps Static API in Google for Developers. Online access on May 19, 2023 at https://developers.google.com/maps/documentation/maps-static/start.
  • Habtezion, N., M. Tahmasebi Nasab, and X. Chu. 2016. “How Does DEM Resolution Affect Microtopographic Characteristics, Hydrologic Connectivity, and Modelling of Hydrologic Processes?” Hydrological Processes 30 (25): 4870–4892. https://doi.org/10.1002/hyp.10967.
  • Hogland, J., N. Anderson, J. St Peter, J. Drake, and P. Medley. 2018. “Mapping Forest Characteristics at Fine Resolution Across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data.” ISPRS International Journal of Geo-Information 7 (4): 140. https://doi.org/10.3390/ijgi7040140.
  • Hu, J., H. Niu, J. Carrasco, B. Lennox, and F. Arvin. 2020. “Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning.” IEEE Transactions on Vehicular Technology 69 (12): 14413–14423. https://doi.org/10.1109/TVT.2020.3034800.
  • Hussain, M., J. J. Bird, and D. R. Faria. 2018. September. “A Study on CNN Transfer Learning for Image Classification”. In UK Workshop on Computational Intelligence 191–202 Cham: Springer. https://doi.org/10.1007/978-3-319-97982-3_16.
  • Iqbal, U., J. Barthelemy, and P. Perez. 2022. “Prediction of Hydraulic Blockage at Culverts from a Single Image Using Deep Learning.” Neural Computing and Applications 34 (23): 1–17. https://doi.org/10.1007/s00521-022-07593-8.
  • Kornblith, S., J. Shlens, and Q. V. Le. 2019. “Do Better Imagenet Models Transfer Better?” In Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR) Long Beach, CA (pp. 2656–2666).
  • Koul, A., C. Becchio, and A. Cavallo. 2018. “Cross-Validation Approaches for Replicability in Psychology.” Frontiers in Psychology 9:1117. https://doi.org/10.3389/fpsyg.2018.01117.
  • Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV (pp. 1097–1105).
  • Kumar, S. 2020. “Data Splitting Technique to Fit Any Machine Learning Model.” Towards Data Science, Aprl 30. Accessed September 11. https://towardsdatascience.com/data-splitting-technique-to-fit-any-machine-learning-model-c0d7f3f1c790.
  • Lindsay, J. B., and K. Dhun. 2015. “Modelling Surface Drainage Patterns in Altered Landscapes Using LiDar.” International Journal of Geographical Information Science 29 (3): 397–411. https://doi.org/10.1080/13658816.2014.975715.
  • Li, R., Z. Tang, X. Li, and J. Winter. 2013. “Drainage Structure Datasets and Effects on LiDAR-Derived Surface Flow Modeling.” ISPRS International Journal of Geo-Information 2 (4): 1136–1152. https://doi.org/10.3390/ijgi2041136.
  • Liu, X., J. Peterson, and Z. Zhang. 2005, December. “High-Resolution DEM Generated from LiDAR Data for Water Resource Management.” In Proceedings of the International Congress on Modelling and Simulation (MODSIM05) (pp. 1402–1408). Melbourne, Australia: Modelling and Simulation Society of Australia and New Zealand Inc.
  • Li, S., L. Xiong, G. Tang, and J. Strobl. 2020. “Deep Learning-Based Approach for Landform Classification from Integrated Data Sources of Digital Elevation Model and Imagery.” Geomorphology 354:107045. https://doi.org/10.1016/j.geomorph.2020.107045.
  • Li, W., B. Zhou, C. Y. Hsu, Y. Li, and F. Ren. 2017, November. “Recognizing Terrain Features on Terrestrial Surface Using a Deep Learning Model: An Example with Crater Detection.” In Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, Los Angeles, California (pp. 33–36).
  • Maxwell, A. E., T. A. Warner, B. C. Vanderbilt, and C. A. Ramezan. 2017. “Land Cover Classification and Feature Extraction from National Agriculture Imagery Program (NAIP) Orthoimagery: A Review.” Photogrammetric Engineering & Remote Sensing 83 (11): 737–747. https://doi.org/10.14358/PERS.83.10.737.
  • McFeeters, S. K. 1996. “The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17 (7): 1425–1432. https://doi.org/10.1080/01431169608948714.
  • Patro, S., and K. K. Sahu. 2015. “Normalization: A Preprocessing Stage.” IARJSET 20–22. arXiv preprint arXiv:1503.06462. https://doi.org/10.17148/IARJSET.2015.2305.
  • Pisner, D. A., and D. M. Schnyer. 2020. “Support Vector Machine.” In Machine Learning, 101–121. Academic Press. https://doi.org/10.1016/B978-0-12-815739-8.00006-7.
  • Poppenga, S. K., and B. B. Worstell. 2016. “Hydrologic Connectivity: Quantitative Assessments of Hydrologic-Enforced Drainage Structures in an Elevation Model.” Journal of Coastal Research 76:90–106. https://doi.org/10.2112/SI76-009.
  • Poppenga, S. K., B. B. Worstell, J. M. Stoker, and S. K. Greenlee. 2010. “Using Selective Drainage Methods to Extract Continuous Surface Flow from 1-Meter Lidar-Derived Digital Elevation Data.” US Geological Survey Scientific Investigations Report 2010–5059 12 p.
  • Pringle, C. M. 2001. “Hydrologic Connectivity and the Management of Biological Reserves: A Global Perspective.” Ecological Applications 11 (4): 981–998. 10.1890/1051-0761(2001)011[0981:HCATMO]2.0.CO;2.
  • Rafique, M. U., J. Zhu, and N. Jacobs. 2022. “Automatic Segmentation of Sinkholes Using a Convolutional Neural Network.” Earth & Space Science 9 (2): e2021EA002195. https://doi.org/10.1029/2021EA002195.
  • Regnauld, N., and W. A. Mackaness. 2006. “Creating a Hydrographic Network from Its Cartographic Representation: A Case Study Using Ordnance Survey MasterMap Data.” International Journal of Geographical Information Science 20 (6): 611–631. https://doi.org/10.1080/13658810600607402.
  • Saranya, C., and G. Manikandan. 2013. “A Study on Normalization Techniques for Privacy Preserving Data Mining.” International Journal of Engineering and Technology (IJET) 5 (3): 2701–2704.
  • Shore, M., P. N. C. Murphy, P. Jordan, P. E. Mellander, M. Kelly-Quinn, M. Cushen, S. Mechan, O. Shine, and A. R. Melland. 2013. “Evaluation of a Surface Hydrological Connectivity Index in Agricultural Catchments.” Environmental Modelling & Software 47:7–15. https://doi.org/10.1016/j.envsoft.2013.04.003.
  • Sinha, R. K., R. Pandey, and R. Pattnaik. 2018. “Deep Learning for Computer Vision Tasks: A Review.” arXiv preprint arXiv: 1804.03928.
  • Sofia, G., G. D. Fontana, and P. Tarolli. 2014. “High‐Resolution Topography and Anthropogenic Feature Extraction: Testing Geomorphometric Parameters in Floodplains.” Hydrological Processes 28 (4): 2046–2061. https://doi.org/10.1002/hyp.9727.
  • Stanislawski, L., T. Brockmeyer, and E. Shavers. 2018. “Automated Road Breaching to Enhance Extraction of Natural Drainage Networks from Elevation Models Through Deep Learning.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42 (4): 597–601. https://doi.org/10.5194/isprs-archives-XLII-4-597-2018.
  • Stieglitz, M., J. Shaman, J. McNamara, V. Engel, J. Shanley, and G. W. Kling. 2003. “An Approach to Understanding Hydrologic Connectivity on the Hillslope and the Implications for Nutrient Transport.” Global Biogeochemical Cycles 17 (4). https://doi.org/10.1029/2003GB002041.
  • Stutheit, R. G., M. C. Gilbert, K. L. Lawrence, and P. M. Whited. 2004. “A Regional Guidebook for Applying the Hydrogeomorphic Approach to Assessing Wetland Functions of Rainwater Basin Depressional Wetlands in Nebraska.” US Army Corps of Engineers 137. https://digitalcommons.unl.edu/usarmyceomaha/137.
  • Suthaharan, S. 2016. “Support Vector Machine.” In Machine Learning Models and Algorithms for Big Data Classification, 207–235. Boston: Springer US. https://doi.org/10.1007/978-1-4899-7641-3_9.
  • Talafha, S., D. Wu, B. Rekabdar, R. Li, and G. Wang. 2021, September. “Classification and Feature Extraction for Hydraulic Structures Data Using Advanced CNN Architectures.” In 2021 Third International Conference on Transdisciplinary AI (TransAI), Laguna Hills, CA (pp. 137–146). IEEE.
  • Tang, X., H. Hong, Y. Shu, H. Tang, J. Li, and W. Liu. 2019. “Urban Waterlogging Susceptibility Assessment Based on a PSO-SVM Method Using a Novel Repeatedly Random Sampling Idea to Select Negative Samples.” Journal of Hydrology 576:583–595. https://doi.org/10.1016/j.jhydrol.2019.06.058.
  • Tang, M., F. Perazzi, A. Djelouah, I. Ben Ayed, C. Schroers, and Y. Boykov 2018. “On Regularized Losses for Weakly-Supervised Cnn Segmentation.” In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany (pp. 507–522).
  • Wang, J., L. Li, Z. Hao, and J. J. Gourley. 2011. “Stream Guiding Algorithm for Deriving Flow Direction from DEM and Location of Main Streams.” IAHS-AISH Publication 346:198–206.
  • Widyantoko, Z., T. P. Widowati, I. Isnaini, and P. Trapsiladi. 2021. “Expert Role in Image Classification Using CNN for Hard to Identify Object: Distinguishing Batik and Its Imitation.” IAES International Journal of Artificial Intelligence (IJ-AI) 10 (1): 93. https://doi.org/10.11591/ijai.v10.i1.pp93-100.
  • Wulder, M. A., T. R. Loveland, D. P. Roy, C. J. Crawford, J. G. Masek, C. E. Woodcock, R. G. Alleng, et al. 2019. “Current Status of Landsat Program, Science, and Applications.” Remote Sensing of Environment 225:127–147. https://doi.org/10.1016/j.rse.2019.02.015.
  • Xu, Z., S. Wang, L. V. Stanislawski, Z. Jiang, N. Jaroenchai, A. M. Sainju, E. Shavers, et al. 2021. “An Attention U-Net Model for Detection of Fine-Scale Hydrologic Streamlines.” Environmental Modelling & Software 140:104992. https://doi.org/10.1016/j.envsoft.2021.104992.
  • Yang, H. L., J. Yuan, D. Lunga, M. Laverdiere, A. Rose, and B. Bhaduri. 2018. “Building Extraction at Scale Using Convolutional Neural Network: Mapping of the United States.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (8): 2600–2614. https://doi.org/10.1109/JSTARS.2018.2835377.
  • Ye, C., Y. Li, P. Cui, L. Liang, S. Pirasteh, J. Marcato, W. N. Gonçalves, et al. 2019. “Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning with Constrains.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (12): 5047–5060. https://doi.org/10.1109/JSTARS.2019.2951725.
  • Yu, H., S. Jiang, and K. C. Land. 2015. “Multicollinearity in Hierarchical Linear Models.” Social Science Research 53:118–136. https://doi.org/10.1016/j.ssresearch.2015.04.008.
  • Zhang, H., P. V. Zimba, and E. U. Nzewi. 2019. “A New Pseudoinvariant Near-Infrared Threshold Method for Relative Radiometric Correction of Aerial Imagery.” Remote Sensing 11 (16).
  • Ziggah, Y. Y., H. Youjian, A. R. Tierra, and P. B. Laari. 2019. “Coordinate Transformation Between Global and Local Data Based on Artificial Neural Network with K-Fold Cross-Validation in Ghana.” Earth Sciences Research Journal 23 (1): 67–77. https://doi.org/10.15446/esrj.v23n1.63860.