{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T04:09:25Z","timestamp":1772510965143,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T00:00:00Z","timestamp":1600905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Robotics will significantly impact large sectors of the economy with relatively low productivity, such as Agri-Food production. Deploying agricultural robots on the farm is still a challenging task. When it comes to localising the robot, there is a need for a preliminary map, which is obtained from a first robot visit to the farm. Mapping is a semi-autonomous task that requires a human operator to drive the robot throughout the environment using a control pad. Visual and geometric features are used by Simultaneous Localisation and Mapping (SLAM) Algorithms to model and recognise places, and track the robot\u2019s motion. In agricultural fields, this represents a time-consuming operation. This work proposes a novel solution\u2014called AgRoBPP-bridge\u2014to autonomously extract Occupancy Grid and Topological maps from satellites images. These preliminary maps are used by the robot in its first visit, reducing the need of human intervention and making the path planning algorithms more efficient. AgRoBPP-bridge consists of two stages: vineyards row detection and topological map extraction. For vineyards row detection, we explored two approaches, one that is based on conventional machine learning technique, by considering Support Vector Machine with Local Binary Pattern-based features, and another one found in deep learning techniques (ResNET and DenseNET). From the vineyards row detection, we extracted an occupation grid map and, by considering advanced image processing techniques and Voronoi diagrams concept, we obtained a topological map. Our results demonstrated an overall accuracy higher than 85% for detecting vineyards and free paths for robot navigation. The Support Vector Machine (SVM)-based approach demonstrated the best performance in terms of precision and computational resources consumption. AgRoBPP-bridge shows to be a relevant contribution to simplify the deployment of robots in agriculture.<\/jats:p>","DOI":"10.3390\/robotics9040077","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T01:39:33Z","timestamp":1600997973000},"page":"77","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Occupancy Grid and Topological Maps Extraction from Satellite Images for Path Planning in Agricultural Robots"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0255-5005","authenticated-orcid":false,"given":"Lu\u00eds Carlos","family":"Santos","sequence":"first","affiliation":[{"name":"INESC-TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, CRIIS\u2014Centre for Robotics in Industry and Intelligent Systems, 4200-465 Porto, Portugal"},{"name":"ECT\u2014School of Sciences and Technologies, UTAD\u2014University of Tr\u00e1s-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6909-0209","authenticated-orcid":false,"given":"Andr\u00e9 Silva","family":"Aguiar","sequence":"additional","affiliation":[{"name":"INESC-TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, CRIIS\u2014Centre for Robotics in Industry and Intelligent Systems, 4200-465 Porto, Portugal"},{"name":"ECT\u2014School of Sciences and Technologies, UTAD\u2014University of Tr\u00e1s-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-6113","authenticated-orcid":false,"given":"Filipe Neves","family":"Santos","sequence":"additional","affiliation":[{"name":"INESC-TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, CRIIS\u2014Centre for Robotics in Industry and Intelligent Systems, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5798-1298","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Valente","sequence":"additional","affiliation":[{"name":"INESC-TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, CRIIS\u2014Centre for Robotics in Industry and Intelligent Systems, 4200-465 Porto, Portugal"},{"name":"ECT\u2014School of Sciences and Technologies, UTAD\u2014University of Tr\u00e1s-os-Montes and Alto Douro, 5001-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7023-8562","authenticated-orcid":false,"given":"Marcelo","family":"Petry","sequence":"additional","affiliation":[{"name":"INESC-TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, CRIIS\u2014Centre for Robotics in Industry and Intelligent Systems, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1098\/rstb.2007.2164","article-title":"Shrink and share: Humanity\u2019s present and future Ecological Footprint","volume":"363","author":"Kitzes","year":"2008","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"76","DOI":"10.11114\/aef.v2i3.909","article-title":"Science and Innovation Strategic Policy Plans for the 2020s (EU,AU,UK): Will They Prepare Us for the World in 2050?","volume":"2","author":"Perry","year":"2015","journal-title":"Appl. Econ. Financ."},{"key":"ref_3","unstructured":"Leshcheva, M., and Ivolga, A. (2018). Human resources for agricultural organizations of agro-industrial region, areas for improvement. Sustainable Agriculture and Rural Development in Terms of the Republic of Serbia Strategic Goals Realization within the Danube Region: Support Programs for the Improvement of Agricultural and Rural Development, 14\u201315 December 2017, Belgrade, Serbia. Thematic Proceedings, Institute of Agricultural Economics."},{"key":"ref_4","first-page":"1","article-title":"Status of agriculture, forestry, fisheries and natural resources human resource in cebu and bohol, central philippines","volume":"19","author":"Rica","year":"2018","journal-title":"J. Agric. Technol. Manag."},{"key":"ref_5","unstructured":"Robotics, E. (2018, April 21). Strategic Research Agenda for Robotics in Europe 2014\u20132020. Available online: Eu-robotics.net\/cms\/upload\/topicgroups\/SRA2020SPARC.pdf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bietresato, M., Carabin, G., D\u2019Auria, D., Gallo, R., Ristorto, G., Mazzetto, F., Vidoni, R., Gasparetto, A., and Scalera, L. (2016, January 29\u201331). A tracked mobile robotic lab for monitoring the plants volume and health. Proceedings of the 2016 12th IEEE\/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Auckland, New Zealand.","DOI":"10.1109\/MESA.2016.7587134"},{"key":"ref_7","unstructured":"Ristorto, G., Gallo, R., Gasparetto, A., Scalera, L., Vidoni, R., and Mazzetto, F. (2017, January 13\u201315). A Mobile Laboratory for Orchard Health Status Monitoring in Precision Frming. Proceedings of the XXXVII CIOSTA & CIGR Section V Conference, Research and innovation for the Sustainable and Safe Management of Agricultural and Forestry Systems, Palermo, Italy."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.compag.2019.01.016","article-title":"Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment","volume":"157","author":"Mahmud","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Iqbal, J., Xu, R., Sun, S., and Li, C. (2020). Simulation of an Autonomous Mobile Robot for LiDAR-Based In-Field Phenotyping and Navigation. Robotics, 9.","DOI":"10.3390\/robotics9020046"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Hellmann Santos, C., and Pekkeriet, E. (2020). Agricultural Robotics for Field Operations. Sensors, 20.","DOI":"10.3390\/s20092672"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s10846-016-0340-5","article-title":"Towards a reliable robot for steep slope vineyards monitoring","volume":"83","author":"Sobreira","year":"2016","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1017\/S0263574719000961","article-title":"Path Planning Aware of Robot\u2019s Center of Mass for Steep Slope Vineyards","volume":"38","author":"Santos","year":"2020","journal-title":"Robotica"},{"key":"ref_13","unstructured":"Seif, G. (2020, July 15). Semantic Segmentation Suite in TensorFlow. Available online: https:\/\/github.com\/GeorgeSeif\/Semantic-Segmentation-Suite."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.5897\/IJPS11.1745","article-title":"Optimal path planning of mobile robots: A review","volume":"7","author":"Raja","year":"2012","journal-title":"Int. J. Phys. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.robot.2016.08.001","article-title":"Heuristic approaches in robot path planning: A survey","volume":"86","author":"Mac","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1016\/j.robot.2013.09.004","article-title":"A survey on coverage path planning for robotics","volume":"61","author":"Galceran","year":"2013","journal-title":"Robot. Auton. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1002\/rob.20285","article-title":"Differentially constrained mobile robot motion planning in state lattices","volume":"26","author":"Pivtoraiko","year":"2009","journal-title":"J. Field Robot."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Karaman, S., Walter, M.R., Perez, A., Frazzoli, E., and Teller, S. (2011, January 9\u201313). Anytime Motion Planning using the RRT*. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shangai, China.","DOI":"10.1109\/ICRA.2011.5980479"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fernandes, E., Costa, P., Lima, J., and Veiga, G. (2015, January 17\u201319). Towards an orientation enhanced astar algorithm for robotic navigation. Proceedings of the 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Spain.","DOI":"10.1109\/ICIT.2015.7125590"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.jocs.2017.08.004","article-title":"Bezier curve based path planning in a dynamic field using modified genetic algorithm","volume":"25","author":"Elhoseny","year":"2018","journal-title":"J. Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Santos, L.C., Santos, F.N., Solteiro Pires, E.J., Valente, A., Costa, P., and Magalh\u00e3es, S. (2020, January 15\u201317). Path Planning for ground robots in agriculture: A short review. Proceedings of the 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Ponta Delgada, Azores, Portugal.","DOI":"10.1109\/ICARSC49921.2020.9096177"},{"key":"ref_22","unstructured":"Mougel, B., Lelong, C., and Nicolas, J. (2009). Classification and information extraction in very high resolution satellite images for tree crops monitoring. Remote Sensing for a Changing Europe, Proceedings of the 28th Symposium of the European Association of Remote Sensing Laboratories, Istanbul, Turkey, 2\u20135 June 2008, IOS Press."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Karakizi, C., Oikonomou, M., and Karantzalos, K. (2016). Vineyard detection and vine variety discrimination from very high resolution satellite data. Remote Sens., 8.","DOI":"10.3390\/rs8030235"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1243\/095440705X34667","article-title":"Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle","volume":"219","author":"Zhang","year":"2005","journal-title":"Proc. Inst. Mech. Eng. Part D J. Automob. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Ortiz, M., Guti\u00e9rrez, P.A., Pe\u00f1a, J.M., Torres-S\u00e1nchez, J., L\u00f3pez-Granados, F., and Herv\u00e1s-Mart\u00ednez, C. (2016, January 6\u20139). Machine learning paradigms for weed mapping via unmanned aerial vehicles. Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Athens, Greece.","DOI":"10.1109\/SSCI.2016.7849987"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.compag.2009.09.012","article-title":"From pixel to vine parcel: A complete methodology for vineyard delineation and characterization using remote-sensing data","volume":"70","author":"Delenne","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","first-page":"65","article-title":"Vine signal extraction\u2014An application of remote sensing in precision viticulture","volume":"31","author":"Smit","year":"2010","journal-title":"S. Afr. J. Enol. Vitic."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Poblete-Echeverr\u00eda, C., Olmedo, G.F., Ingram, B., and Bardeen, M. (2017). Detection and segmentation of vine canopy in ultra-high spatial resolution RGB imagery obtained from unmanned aerial vehicle (UAV): A case study in a commercial vineyard. Remote Sens., 9.","DOI":"10.3390\/rs9030268"},{"key":"ref_29","unstructured":"Nolan, A., Park, S., Fuentes, S., Ryu, D., and Chung, H. (December, January 29). Automated detection and segmentation of vine rows using high resolution UAS imagery in a commercial vineyard. Proceedings of the 21st International Congress on Modelling and Simulation, Gold Coast, Australia."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.compag.2015.03.011","article-title":"Vineyard detection from unmanned aerial systems images","volume":"114","author":"Comba","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","unstructured":"(2020, August 30). Quinta do Seixo at Sogrape. Available online: https:\/\/eng.sograpevinhos.com\/regioes\/Douro\/locais\/QuintadoSeixo."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/0921-8890(91)90014-C","article-title":"A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations","volume":"8","author":"Kuipers","year":"1991","journal-title":"Robot. Auton. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Luo, R.C., and Shih, W. (2019, January 11\u201313). Topological map Generation for Intrinsic Visual Navigation of an Intelligent Service Robot. Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2019.8662062"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Joo, K., Lee, T., Baek, S., and Oh, S. (2010, January 11\u201313). Generating topological map from occupancy grid-map using virtual door detection. Proceedings of the IEEE Congress on Evolutionary Computation, Las Vegas, NV, USA.","DOI":"10.1109\/CEC.2010.5586510"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/S0004-3702(97)00078-7","article-title":"Learning metric-topological maps for indoor mobile robot navigation","volume":"99","author":"Thrun","year":"1998","journal-title":"Artif. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Brunskill, E., Kollar, T., and Roy, N. (November, January 29). Topological mapping using spectral clustering and classification. Proceedings of the 2007 IEEE\/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA.","DOI":"10.1109\/IROS.2007.4399611"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Konolige, K., Marder-Eppstein, E., and Marthi, B. (2011, January 9\u201313). Navigation in hybrid metric-topological maps. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980074"},{"key":"ref_38","unstructured":"Correia, L., Reis, L.P., and Cascalho, J. (2013). Towards Extraction of Topological maps from 2D and 3D Occupancy Grids. Progress in Artificial Intelligence, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Santos, L., Santos, F.N., Magalh\u00e3es, S., Costa, P., and Reis, R. (2019, January 24\u201326). Path Planning approach with the extraction of Topological maps from Occupancy Grid Maps in steep slope vineyards. Proceedings of the 2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Porto, Portugal.","DOI":"10.1109\/ICARSC.2019.8733630"},{"key":"ref_40","unstructured":"Moura Oliveira, P., Novais, P., and Reis, L.P. (2019). Vineyard Segmentation from Satellite Imagery Using Machine Learning. Progress in Artificial Intelligence, Springer International Publishing."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","article-title":"A comparative study of texture measures with classification based on featured distributions","volume":"29","author":"Ojala","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_42","unstructured":"Liu, Y., and Zheng, Y.F. (August, January 31). One-against-all multi-class SVM classification using reliability measures. Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, QC, Canada."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_46","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Caesars Palace, Las Vegas, NV, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jing, J., Wang, Z., R\u00e4tsch, M., and Zhang, H. (2020). Mobile-Unet: An efficient convolutional neural network for fabric defect detection. Text. Res. J.","DOI":"10.1177\/0040517520928604"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y. (2017, January 21\u201326). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lau, B., Sprunk, C., and Burgard, W. (2010, January 18\u201322). Improved updating of Euclidean distance maps and Voronoi diagrams. Proceedings of the 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5650794"},{"key":"ref_50","unstructured":"(2020, July 02). Map Puzzel Tool for Google Maps. Available online: http:\/\/www.mappuzzle.se\/."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.compag.2019.03.027","article-title":"End-to-end sequence labeling via deep learning for automatic extraction of agricultural regulations","volume":"162","author":"Lacasta","year":"2019","journal-title":"Comput. Electron. Agric."}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/9\/4\/77\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:13:24Z","timestamp":1760177604000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/9\/4\/77"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,24]]},"references-count":51,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["robotics9040077"],"URL":"https:\/\/doi.org\/10.3390\/robotics9040077","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,24]]}}}