{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T00:04:32Z","timestamp":1770422672461,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Vegetation encroachment in power line corridors has multiple problems for modern energy-dependent societies. Failures due to the contact between power lines and vegetation can result in power outages and millions of dollars in losses. To address this problem, UAVs have emerged as a promising solution due to their ability to quickly and affordably monitor long corridors through autonomous flights or being remotely piloted. However, the extensive and manual task that requires analyzing every image acquired by the UAVs when searching for the existence of vegetation encroachment has led many authors to propose the use of Deep Learning to automate the detection process. Despite the advantages of using a combination of UAV imagery and Deep Learning, there is currently a lack of datasets that help to train Deep Learning models for this specific problem. This paper presents a dataset for the semantic segmentation of vegetation encroachment in power line corridors. RGB orthomosaics were obtained for a rural road area using a commercial UAV. The dataset is composed of pairs of tessellated RGB images, coming from the orthomosaic and corresponding multi-color masks representing three different classes: vegetation, power lines, and the background. A detailed description of the image acquisition process is provided, as well as the labeling task and the data augmentation techniques, among other relevant details to produce the dataset. Researchers would benefit from using the proposed dataset by developing and improving strategies for vegetation encroachment monitoring using UAVs and Deep Learning.<\/jats:p>","DOI":"10.3390\/data8080128","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T09:27:48Z","timestamp":1691141268000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["VEPL Dataset: A Vegetation Encroachment in Power Line Corridors Dataset for Semantic Segmentation of Drone Aerial Orthomosaics"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9988-4624","authenticated-orcid":false,"given":"Mateo","family":"Cano-Solis","sequence":"first","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050041, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7369-8399","authenticated-orcid":false,"given":"John R.","family":"Ballesteros","sequence":"additional","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050041, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0378-028X","authenticated-orcid":false,"given":"John W.","family":"Branch-Bedoya","sequence":"additional","affiliation":[{"name":"Facultad de Minas, Universidad Nacional de Colombia, Medell\u00edn 050041, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.epsr.2012.07.015","article-title":"Vegetation encroachment monitoring for transmission lines right-of-ways: A survey","volume":"95","author":"Ahmad","year":"2013","journal-title":"Electr. Power Syst. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.ijepes.2017.12.016","article-title":"Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning","volume":"99","author":"Nguyen","year":"2018","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., and Xu, W. (2016, January 27\u201330). CNN-RNN: A unified framework for multi-label image classification. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.251"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Azevedo, F., Dias, A., Almeida, J., Oliveira, A., Ferreira, A., Santos, T., Martins, A., and Silva, E. (2019, January 24\u201326). Real-Time LiDAR-based Power Lines Detection for Unmanned Aerial Vehicles. Proceedings of the 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Porto, Portugal.","DOI":"10.1109\/ICARSC.2019.8733646"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s10044-014-0391-9","article-title":"A novel method for vegetation encroachment monitoring of transmission lines using a single 2D camera","volume":"18","author":"Ahmad","year":"2015","journal-title":"Pattern Anal. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"146281","DOI":"10.1109\/ACCESS.2021.3123158","article-title":"Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management","volume":"9","author":"Odo","year":"2021","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TPWRD.2021.3059307","article-title":"Automated Power Lines Vegetation Monitoring Using High-Resolution Satellite Imagery","volume":"37","author":"Gazzea","year":"2022","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Haroun, F.M.E., Deros, S.N.M., Bin Baharuddin, M.Z., and Din, N.M. (2021). Detection of Vegetation Encroachment in Power Transmission Line Corridor from Satellite Imagery Using Support Vector Machine: A Features Analysis Approach. Energies, 14.","DOI":"10.3390\/en14123393"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Han, Y., Han, J., Ni, Z., Wang, W., and Jiang, H. (2021, January 26). Instance Segmentation of Transmission Line Images Based on an Improved D-SOLO Network. Proceedings of the 2021 IEEE 3rd International Conference on Power Data Science (ICPDS), Harbin, China.","DOI":"10.1109\/ICPDS54746.2021.9689924"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s00138-020-01138-6","article-title":"LS-Net: Fast single-shot line-segment detector","volume":"32","author":"Nguyen","year":"2020","journal-title":"Mach. Vis. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, R., Yang, B., Xiao, W., Liang, F., Liu, Y., and Wang, Z. (2019). Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds. Remote Sens., 11.","DOI":"10.3390\/rs11222600"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.arcontrol.2020.09.002","article-title":"Data analysis in visual power line inspection: An in-depth review of deep learning for component detection and fault diagnosis","volume":"50","author":"Liu","year":"2020","journal-title":"Annu. Rev. Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning Hierarchical Features for Scene Labeling","volume":"35","author":"Farabet","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","unstructured":"Thoma, M. (2016). A Survey of Semantic Segmentation. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ballesteros, J.R., Sanchez-Torres, G., and Branch-Bedoya, J.W. (2022). HAGDAVS: Height-Augmented Geo-Located Dataset for Detection and Semantic Segmentation of Vehicles in Drone Aerial Orthomosaics. Data, 7.","DOI":"10.3390\/data7040050"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ballesteros, J.R., Sanchez-Torres, G., and Branch, J.W. (2021, January 25\u201327). Automatic road extraction in small urban areas of developing countries using drone imagery and Image Translation. Proceedings of the 2021 2nd Sustainable Cities Latin America Conference (SCLA), Medellin, Colombia.","DOI":"10.1109\/SCLA53004.2021.9540111"},{"key":"ref_18","unstructured":"Restrepo, Z., Botero, S., Gonz\u00e1lez-Caro, S., Ortiz-Yusty, C., and Alvarez-Davila, E. (2018). Gu\u00eda de Flora y Fauna del Sistema Local de \u00c1rea Protegidas de Envigado (Colombia), Editorial Jard\u00edn Bot\u00e1nico de Medell\u00edn."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ballesteros, J.R., Sanchez-Torres, G., and Branch-Bedoya, J.W. (2022). A GIS Pipeline to Produce GeoAI Datasets from Drone Overhead Imagery. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11100508"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_21","unstructured":"(2023, April 02). Agisoft Metashape Professional (Version 1.6.3) (Software). Available online: https:\/\/www.agisoft.com."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Torralba, A., and Efros, A.A. (2011, January 20\u201325). Unbiased Look at Dataset Bias. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and Flexible Image Augmentations. Information, 11.","DOI":"10.3390\/info11020125"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q.V. (2019, January 15\u201320). AutoAugment: Learning Augmentation Strategies From Data. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk, A., and Grochowski, M. (2018, January 9\u201312). Data augmentation for improving deep learning in image classification problem. Proceedings of the International Interdisciplinary PhD Workshop (IIPhDW), Swinoujscie, Poland.","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Taylor, L., and Nitschke, G. (2018, January 18\u201321). Improving Deep Learning with Generic Data Augmentation. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628742"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/8\/8\/128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:25:51Z","timestamp":1760127951000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/8\/8\/128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":27,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["data8080128"],"URL":"https:\/\/doi.org\/10.3390\/data8080128","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,4]]}}}