{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T21:57:39Z","timestamp":1781906259047,"version":"3.54.5"},"reference-count":37,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U22A20620"],"award-info":[{"award-number":["U22A20620"]}]},{"name":"National Natural Science Foundation of China","award":["U22A20620\/003"],"award-info":[{"award-number":["U22A20620\/003"]}]},{"name":"National Natural Science Foundation of China","award":["2023C003"],"award-info":[{"award-number":["2023C003"]}]},{"name":"National Natural Science Foundation of China","award":["GCCRC202301"],"award-info":[{"award-number":["GCCRC202301"]}]},{"name":"PI project of the Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains","award":["U22A20620"],"award-info":[{"award-number":["U22A20620"]}]},{"name":"PI project of the Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains","award":["U22A20620\/003"],"award-info":[{"award-number":["U22A20620\/003"]}]},{"name":"PI project of the Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains","award":["2023C003"],"award-info":[{"award-number":["2023C003"]}]},{"name":"PI project of the Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains","award":["GCCRC202301"],"award-info":[{"award-number":["GCCRC202301"]}]},{"name":"Henan Polytechnic University Surveying and Mapping Science and Technology \u201cDouble first-class\u201d discipline creation and cultivation project","award":["U22A20620"],"award-info":[{"award-number":["U22A20620"]}]},{"name":"Henan Polytechnic University Surveying and Mapping Science and Technology \u201cDouble first-class\u201d discipline creation and cultivation project","award":["U22A20620\/003"],"award-info":[{"award-number":["U22A20620\/003"]}]},{"name":"Henan Polytechnic University Surveying and Mapping Science and Technology \u201cDouble first-class\u201d discipline creation and cultivation project","award":["2023C003"],"award-info":[{"award-number":["2023C003"]}]},{"name":"Henan Polytechnic University Surveying and Mapping Science and Technology \u201cDouble first-class\u201d discipline creation and cultivation project","award":["GCCRC202301"],"award-info":[{"award-number":["GCCRC202301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Coal mining in the Loess Plateau can very easily generate ground cracks, and these cracks can immediately result in ventilation trouble under the mine shaft, runoff disturbance, and vegetation destruction. Advanced UAV (Unmanned Aerial Vehicle) high-resolution mapping and DL (Deep Learning) are introduced as the key methods to quickly delineate coal mining ground surface cracks for disaster prevention. Firstly, the dataset named the Ground Cracks of Coal Mining Area Unmanned Aerial Vehicle (GCCMA-UAV) is built, with a ground resolution of 3 cm, which is suitable to make a 1:500 thematic map of the ground crack. This GCCMA-UAV dataset includes 6280 images of ground cracks, and the size of the imagery is 256 \u00d7 256 pixels. Secondly, the DRA-UNet model is built effectively for coal mining ground surface crack delineation. This DRA-UNet model is an improved UNet DL model, which mainly includes the DAM (Dual Dttention Dechanism) module, the RN (residual network) module, and the ASPP (Atrous Spatial Pyramid Pooling) module. The DRA-UNet model shows the highest recall rate of 77.29% when the DRA-UNet was compared with other similar DL models, such as DeepLabV3+, SegNet, PSPNet, and so on. DRA-UNet also has other relatively reliable indicators; the precision rate is 84.92% and the F1 score is 78.87%. Finally, DRA-UNet is applied to delineate cracks on a DOM (Digital Orthophoto Map) of 3 km2 in the mining workface area, with a ground resolution of 3 cm. There were 4903 cracks that were delineated from the DOM in the Huojitu Coal Mine Shaft. This DRA-UNet model effectively improves the efficiency of crack delineation.<\/jats:p>","DOI":"10.3390\/s24175760","type":"journal-article","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T02:34:06Z","timestamp":1725503646000},"page":"5760","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["DRA-UNet for Coal Mining Ground Surface Crack Delineation with UAV High-Resolution Images"],"prefix":"10.3390","volume":"24","author":[{"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"Shendong Coal Branch, China Shenhua Energy Co., Ltd., Yulin 719000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3005-2819","authenticated-orcid":false,"given":"Weibing","family":"Du","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4389-4678","authenticated-orcid":false,"given":"Xiangyang","family":"Song","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sushe","family":"Chen","sequence":"additional","affiliation":[{"name":"Shendong Coal Branch, China Shenhua Energy Co., Ltd., Yulin 719000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shendong Coal Branch, China Shenhua Energy Co., Ltd., Yulin 719000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hebing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youfeng","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junlin","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoying","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1007\/s10064-017-1108-2","article-title":"Formation and Development Mechanism of Ground Crack Caused by Coal Mining: Effects of Overlying Key Strata","volume":"78","author":"Dawei","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Guo, W., Guo, M., Tan, Y., Bai, E., and Zhao, G. (2019). Sustainable Development of Resources and the Environment: Mining-Induced Eco-Geological Environmental Damage and Mitigation Measures\u2014A Case Study in the Henan Coal Mining Area, China. Sustainability, 11.","DOI":"10.3390\/su11164366"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105189","DOI":"10.1016\/j.enggeo.2019.105189","article-title":"A Typical Earth Fissure Resulting from Loess Collapse on the Loess Plateau in the Weihe Basin, China","volume":"259","author":"Lu","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.psep.2019.04.014","article-title":"A Method for Evaluating the Spontaneous Combustion of Coal by Monitoring Various Gases","volume":"126","author":"Guo","year":"2019","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Habib, M.A., and Khan, R. (2021). Environmental Impacts of Coal-Mining and Coal-Fired Power-Plant Activities in a Developing Country with Global Context. Spatial Modeling and Assessment of Environmental Contaminants: Risk Assessment and Remediation, Springer.","DOI":"10.1007\/978-3-030-63422-3_24"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2835","DOI":"10.1007\/s00603-018-1726-4","article-title":"Ground Subsidence and Surface Cracks Evolution from Shallow-Buried Close-Distance Multi-Seam Mining: A Case Study in Bulianta Coal Mine","volume":"52","author":"Yang","year":"2019","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Fu, Y., Shang, J., Hu, Z., Li, P., Yang, K., Chen, C., Guo, J., and Yuan, D. (2021). Ground Fracture Development and Surface Fracture Evolution in N00 Method Shallowly Buried Thick Coal Seam Mining in an Arid Windy and Sandy Area: A Case Study of the Ningtiaota Mine (China). Energies, 14.","DOI":"10.3390\/en14227712"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1007\/s40789-019-00264-5","article-title":"A Review of UAV Monitoring in Mining Areas: Current Status and Future Perspectives","volume":"6","author":"Ren","year":"2019","journal-title":"Int. J. Coal Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, F., Hu, Z., Fu, Y., Yang, K., Wu, Q., and Feng, Z. (2020). A New Identification Method for Surface Cracks from UAV Images Based on Machine Learning in Coal Mining Areas. Remote Sens., 12.","DOI":"10.3390\/rs12101571"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"126162","DOI":"10.1016\/j.conbuildmat.2021.126162","article-title":"A Critical Review and Comparative Study on Image Segmentation-Based Techniques for Pavement Crack Detection","volume":"321","author":"Kheradmandi","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103858","DOI":"10.1016\/j.earscirev.2021.103858","article-title":"Deep Learning for Geological Hazards Analysis: Data, Models, Applications, and Opportunities","volume":"223","author":"Ma","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.neucom.2022.06.083","article-title":"Recent Advances on Image Edge Detection: A Comprehensive Review","volume":"503","author":"Jing","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_13","unstructured":"Fan, Z., Wu, Y., Lu, J., and Li, W. (2018). Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"04019040","DOI":"10.1061\/(ASCE)CP.1943-5487.0000854","article-title":"Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks","volume":"33","author":"Alipour","year":"2019","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fan, Z., Li, C., Chen, Y., Wei, J., Loprencipe, G., Chen, X., and Di Mascio, P. (2020). Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture. Materials, 13.","DOI":"10.3390\/ma13132960"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"53034","DOI":"10.1109\/ACCESS.2020.2981370","article-title":"Ground Crack Recognition Based on Fully Convolutional Network with Multi-Scale Input","volume":"8","author":"Cheng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.patrec.2021.02.005","article-title":"Mixed Pooling and Richer Attention Feature Fusion for Crack Detection","volume":"145","author":"Zhou","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chen, W., Zhong, C., Qin, X., and Wang, L. (2023). Deep Learning Based Intelligent Recognition of Ground Fissures. Intelligent Interpretation for Geological Disasters: From Space-Air-Ground Integration Perspective, Springer.","DOI":"10.1007\/978-981-99-5822-1"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","article-title":"Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.neucom.2019.01.036","article-title":"DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation","volume":"338","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_21","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_23","unstructured":"Agarap, A.F. (2018). Deep Learning Using Rectified Linear Units (Relu). arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., and Jorge Cardoso, M. (2017, January 14). Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Qu\u00e9bec City, QC, Canada. Proceedings 3."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/ACCESS.2019.2962617","article-title":"The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling","volume":"8","author":"Ho","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_31","first-page":"12077","article-title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","unstructured":"Poudel, R.P., Liwicki, S., and Cipolla, R. (2019). Fast-Scnn: Fast Semantic Segmentation Network. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107474","DOI":"10.1016\/j.patcog.2020.107474","article-title":"A Novel Hybrid Approach for Crack Detection","volume":"107","author":"Fang","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"18392","DOI":"10.1109\/TITS.2022.3158670","article-title":"DMA-Net: DeepLab with Multi-Scale Attention for Pavement Crack Segmentation","volume":"23","author":"Sun","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_35","first-page":"5027920","article-title":"DDR-Unet: A High Accuracy and Efficient Ore Image Segmentation Method","volume":"72","author":"Li","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_36","first-page":"103788","article-title":"GFSegNet: A Multi-Scale Segmentation Model for Mining Area Ground Fissures","volume":"128","author":"Chen","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","first-page":"103039","article-title":"MFPA-Net: An Efficient Deep Learning Network for Automatic Ground Fissures Extraction in UAV Images of the Coal Mining Area","volume":"114","author":"Jiang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5760\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:48:46Z","timestamp":1760111326000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5760"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,4]]},"references-count":37,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175760"],"URL":"https:\/\/doi.org\/10.3390\/s24175760","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,4]]}}}