{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T21:03:24Z","timestamp":1763499804392,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT","award":["PCIF\/SSI\/0102\/2017"],"award-info":[{"award-number":["PCIF\/SSI\/0102\/2017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape\u2019s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang\u2013Suen and Guo\u2013Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications.<\/jats:p>","DOI":"10.3390\/rs15010271","type":"journal-article","created":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T02:05:53Z","timestamp":1672711553000},"page":"271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5767-3394","authenticated-orcid":false,"given":"Miguel","family":"Louren\u00e7o","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, NOVA School of Science and Technology (FCT NOVA), NOVA University Lisbon, 2825-149 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5150-6421","authenticated-orcid":false,"given":"Diogo","family":"Estima","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, NOVA School of Science and Technology (FCT NOVA), NOVA University Lisbon, 2825-149 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8687-4291","authenticated-orcid":false,"given":"Henrique","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Telecommunications Institute, 1049-001 Lisbon, Portugal"},{"name":"Polytechnic Institute of Beja, 7800-295 Beja, Portugal"}]},{"given":"Lu\u00eds","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, NOVA School of Science and Technology (FCT NOVA), NOVA University Lisbon, 2825-149 Caparica, Portugal"},{"name":"Centre for Technologies and Systems (UNINOVA-CTS), 2829-516 Caparica, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1354-4739","authenticated-orcid":false,"given":"Andr\u00e9","family":"Mora","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, NOVA School of Science and Technology (FCT NOVA), NOVA University Lisbon, 2825-149 Caparica, Portugal"},{"name":"Centre for Technologies and Systems (UNINOVA-CTS), 2829-516 Caparica, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"ref_1","unstructured":"(2020). National Forestry Accounting Plan\u2014Portugal 2021\u20132025, Ag\u00eancia Portuguesa do Ambiente."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jesus, T.C., Costa, D.G., Portugal, P., and Vasques, F. (2022). A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks. Future Internet, 14.","DOI":"10.3390\/fi14070213"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pereira-Pires, J.E., Aubard, V., Ribeiro, R.A., Fonseca, J.M., Silva, J.M.N., and Mora, A. (2020). Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12060909"},{"key":"ref_4","unstructured":"(2022, June 15). Bee2FireDetection. Early Fire Detection and Decision Support System. Available online: https:\/\/www.ceb-solutions.com\/products\/bee2firedetection\/."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xie, L., and Yuan, T. (2012, January 6\u20138). Monitoring system for forest fire based on wireless sensor network. Proceedings of the 10th World Congress on Intelligent Control and Automation, Beijing, China.","DOI":"10.1109\/WCICA.2012.6359191"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Louren\u00e7o, M., Oliveira, L.B., Oliveira, J.P., Mora, A., Oliveira, H., and Santos, R. (2021). An Integrated Decision Support System for Improving Wildfire Suppression Management. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080497"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"819","DOI":"10.3390\/smartcities4020042","article-title":"Disaster Management in Smart Cities","volume":"4","author":"Elvas","year":"2021","journal-title":"Smart Cities"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Zhang, X., Sun, Y., and Zhang, P. (2018). Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity. Remote Sens., 10.","DOI":"10.3390\/rs10081284"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8919","DOI":"10.1109\/TGRS.2020.2991733","article-title":"Simultaneous Road Surface and Centerline Extraction From Large-Scale Remote Sensing Images Using CNN-Based Segmentation and Tracing","volume":"58","author":"Wei","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning Deconvolution Network for Semantic Segmentation. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sun, S., Xia, W., Zhang, B., and Zhang, Y. (August, January 28). Road Centerlines Extraction from High Resolution Remote Sensing Image. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898382"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Seferbekov, S., Iglovikov, V., and Shvets, A. (2018, January 18\u201323). Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00035"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4451","DOI":"10.1109\/JSTARS.2020.3014242","article-title":"A Self-Supervised Learning Framework for Road Centerline Extraction From High-Resolution Remote Sensing Images","volume":"13","author":"Guo","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","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_15","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 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_16","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":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2014). Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. Computer Vision\u2014ECCV 2014, Springer.","DOI":"10.1007\/978-3-319-10578-9_23"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, T., Comer, M., and Zerubia, J. (2019, January 22\u201325). Feature Extraction and Tracking of CNN Segmentations for Improved Road Detection from Satellite Imagery. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803355"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9362","DOI":"10.1109\/TGRS.2019.2926397","article-title":"Multi-Scale and Multi-Task Deep Learning Framework for Automatic Road Extraction","volume":"57","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"8861886","DOI":"10.1155\/2020\/8861886","article-title":"An Encoder-Decoder Network Based FCN Architecture for Semantic Segmentation","volume":"2020","author":"Xing","year":"2020","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1145\/357994.358023","article-title":"A fast parallel algorithm for thinning digital patterns","volume":"27","author":"Zhang","year":"1984","journal-title":"Commun. ACM"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1145\/62065.62074","article-title":"Parallel thinning with two-subiteration algorithms","volume":"32","author":"Guo","year":"1989","journal-title":"Commun. ACM"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fernandez, H.M., Granja-Martins, F.M., Pedras, C.M.G., Fernandes, P., and Isidoro, J.M.G.P. (2021). An Assessment of Forest Fires and CO2 Gross Primary Production from 1991 to 2019 in Ma\u00e7\u00e3o (Portugal). Sustainability, 13.","DOI":"10.3390\/su13115816"},{"key":"ref_25","unstructured":"(2022, July 28). Direc\u00e7\u00e3o Geral do Territ\u00f3rio, Available online: https:\/\/www.dgterritorio.gov.pt\/."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Computer Vision\u2014ECCV 2018, Springer.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_27","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Kai, L., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_32","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations\u2014ICLR 2015, San Diego, CA, USA."},{"key":"ref_33","first-page":"57","article-title":"Evaluation of Automatic Road Extraction","volume":"32","author":"Heipke","year":"1997","journal-title":"Int. Arch. Photogram. Remote Sens."},{"key":"ref_34","first-page":"105","article-title":"Analysis of automatic road extraction results from airborne SAR imagery","volume":"37","author":"Wessel","year":"2003","journal-title":"Int. Arch. Photogrammetry, Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shao, Z., Zhou, Z., Huang, X., and Zhang, Y. (2021). MRENet: Simultaneous Extraction of Road Surface and Road Centerline in Complex Urban Scenes from Very High-Resolution Images. Remote Sens., 13.","DOI":"10.3390\/rs13020239"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/271\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:56:31Z","timestamp":1760118991000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,2]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010271"],"URL":"https:\/\/doi.org\/10.3390\/rs15010271","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,1,2]]}}}