{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:12:44Z","timestamp":1765609964578,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,30]],"date-time":"2019-11-30T00:00:00Z","timestamp":1575072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003069","name":"Instituto Polit\u00e9cnico Nacional","doi-asserted-by":"publisher","award":["20190077, 20195886, 20196405"],"award-info":[{"award-number":["20190077, 20195886, 20196405"]}],"id":[{"id":"10.13039\/501100003069","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","award":["5241"],"award-info":[{"award-number":["5241"]}],"id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]},{"name":"C\u00e1tedras Conacyt","award":["556"],"award-info":[{"award-number":["556"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nowadays, more than half of the world\u2019s population lives in urban areas, and this number continues increasing. Consequently, there are more and more scientific publications that analyze health problems of people associated with living in these highly urbanized locations. In particular, some of the recent work has focused on relating people\u2019s health to the quality and quantity of urban green areas. In this context, and considering the huge amount of land area in large cities that must be supervised, our work seeks to develop a deep learning-based solution capable of determining the level of health of the land and to assess whether it is contaminated. The main purpose is to provide health institutions with software capable of creating updated maps that indicate where these phenomena are presented, as this information could be very useful to guide public health goals in large cities. Our software is released as open source code, and the data used for the experiments presented in this paper are also freely available.<\/jats:p>","DOI":"10.3390\/s19235287","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T10:50:45Z","timestamp":1575283845000},"page":"5287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1028-9197","authenticated-orcid":false,"given":"Marco","family":"Moreno-Armend\u00e1riz","sequence":"first","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Centro de Investigaci\u00f3n en Computaci\u00f3n, Av. Juan de Dios B\u00e1tiz s\/n, Ciudad de M\u00e9xico 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2836-2102","authenticated-orcid":false,"given":"Hiram","family":"Calvo","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Centro de Investigaci\u00f3n en Computaci\u00f3n, Av. Juan de Dios B\u00e1tiz s\/n, Ciudad de M\u00e9xico 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2734-7560","authenticated-orcid":false,"given":"Carlos","family":"Duchanoy","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Centro de Investigaci\u00f3n en Computaci\u00f3n, Av. Juan de Dios B\u00e1tiz s\/n, Ciudad de M\u00e9xico 07738, Mexico"},{"name":"C\u00e1tedra CONACyT, Instituto Polit\u00e9cnico Nacional, Centro de Investigaci\u00f3n en Computaci\u00f3n, Av. Juan de Dios B\u00e1tiz s\/n, Ciudad de M\u00e9xico 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1624-1234","authenticated-orcid":false,"given":"Anayantzin","family":"L\u00f3pez-Ju\u00e1rez","sequence":"additional","affiliation":[{"name":"Escuela Superior de C\u00f3mputo, Instituto Polit\u00e9cnico Nacional, Av. Juan de Dios B\u00e1tiz s\/n, Col. Lindavista, Ciudad de M\u00e9xico 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5531-7150","authenticated-orcid":false,"given":"Israel","family":"Vargas-Monroy","sequence":"additional","affiliation":[{"name":"Escuela Superior de C\u00f3mputo, Instituto Polit\u00e9cnico Nacional, Av. Juan de Dios B\u00e1tiz s\/n, Col. Lindavista, Ciudad de M\u00e9xico 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2258-7426","authenticated-orcid":false,"given":"Miguel","family":"Suarez-Casta\u00f1on","sequence":"additional","affiliation":[{"name":"Escuela Superior de C\u00f3mputo, Instituto Polit\u00e9cnico Nacional, Av. Juan de Dios B\u00e1tiz s\/n, Col. Lindavista, Ciudad de M\u00e9xico 07738, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1093\/aob\/mcs209","article-title":"The role of grasslands in food security and climate change","volume":"110","year":"2012","journal-title":"Ann. Bot."},{"key":"ref_2","unstructured":"Jacobs, S.W., Whalley, R., and Wheeler, D.J. (2008). Grasses of New South Wales, University of New England Botany."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.scitotenv.2005.08.035","article-title":"Biodiversity in urban habitat patches","volume":"360","author":"Angold","year":"2006","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.landurbplan.2014.01.017","article-title":"Urban green space, public health, and environmental justice: The challenge of making cities \u2018just green enough\u2019","volume":"125","author":"Wolch","year":"2014","journal-title":"Landscape Urban Plann."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s00038-009-0069-z","article-title":"Landscape and well-being: A scoping study on the health-promoting impact of outdoor environments","volume":"55","author":"Abraham","year":"2010","journal-title":"Int. J. Publ. Health"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kondo, M., Fluehr, J., McKeon, T., and Branas, C. (2018). Urban green space and its impact on human health. Int. J. Environ. Res. Publ. Health, 15.","DOI":"10.3390\/ijerph15030445"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1289\/ehp.1510363","article-title":"Exposure to greenness and mortality in a nationwide prospective cohort study of women","volume":"124","author":"James","year":"2016","journal-title":"Environ. Health Perspect."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7937","DOI":"10.1073\/pnas.1503402112","article-title":"Green spaces and cognitive development in primary schoolchildren","volume":"112","author":"Dadvand","year":"2015","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e289","DOI":"10.1016\/S2542-5196(17)30118-3","article-title":"Urban greenness and mortality in Canada\u2019s largest cities: A national cohort study","volume":"1","author":"Crouse","year":"2017","journal-title":"Lancet Planet. Health"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.healthplace.2014.05.001","article-title":"Food environment, walkability, and public open spaces are associated with incident development of cardio-metabolic risk factors in a biomedical cohort","volume":"28","author":"Paquet","year":"2014","journal-title":"Health Place"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.wasman.2016.05.018","article-title":"Forecasting municipal solid waste generation using artificial intelligence modelling approaches","volume":"56","author":"Abbasi","year":"2016","journal-title":"Waste Manag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Souza, J.T.D., Francisco, A.C.D., Piekarski, C.M., and Prado, G.F.D. (2019). Data mining and machine learning to promote smart cities: A systematic review from 2000 to 2018. Sustainability, 11.","DOI":"10.3390\/su11041077"},{"key":"ref_13","first-page":"321","article-title":"Remote sensing of sustainable rural-urban land use in Mexico City: A qualitative analysis for reliability and validity","volume":"3","author":"Rosso","year":"2015","journal-title":"Interdisciplina"},{"key":"ref_14","unstructured":"Trimble (2019, February 09). eCognition Software. Available online: http:\/\/www.ecognition.com\/."},{"key":"ref_15","unstructured":"De la CDMX, P.A.O.T. (2019, February 09). Sistema de Informaci\u00f3n del Patrimonio Ambiental y Urbano de la CDMX. Available online: http:\/\/200.38.34.15:8008\/mapguide\/sig\/siginterno.php."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/LGRS.2016.2542358","article-title":"Convolutional neural network based automatic object detection on aerial images","volume":"13","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1007\/s11629-016-3950-2","article-title":"Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning","volume":"14","author":"Lu","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_18","unstructured":"Do, D., Pham, F., Raheja, A., and Bhandari, S. (2018, January 15\u201319). Machine learning techniques for the assessment of citrus plant health using UAV-based digital images. Proceedings of the Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III, Orlando, FL, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Phan, C., Raheja, A., Bhandari, S., Green, R.L., and Do, D. (2017, January 9\u201313). A predictive model for turfgrass color and quality evaluation using deep learning and UAV imageries. Proceedings of the Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, Anaheim, CA, USA.","DOI":"10.1117\/12.2262042"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ding, K., Raheja, A., Bhandari, S., and Green, R.L. (2016, January 17\u201321). Application of machine learning for the evaluation of turfgrass plots using aerial images. Proceedings of the Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, Baltimore, MD, USA.","DOI":"10.1117\/12.2228695"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"UAV remote sensing for urban vegetation mapping using random forest and texture analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.compag.2017.10.027","article-title":"Weed detection in soybean crops using ConvNets","volume":"143","author":"Freitas","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","unstructured":"DJI (2019, August 28). PHANTOM 4.2019. Available online: https:\/\/www.dji.com\/mx\/phantom-4."},{"key":"ref_24","unstructured":"Vargas, I. (2019, March 26). Deep Green Diagnostics, Mendeley Data. Available online: http:\/\/dx.doi.org\/10.17632\/dn8rj26kzm.4."},{"key":"ref_25","first-page":"1995","article-title":"Convolutional networks for images, speech, and time series","volume":"3361","author":"LeCun","year":"1995","journal-title":"Handb. Brain Theor. Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_28","unstructured":"Lopez, A. (2019, September 10). Deep Green Diagnostics, Github. Available online: https:\/\/github.com\/AnayantzinPao\/DeepGreenDiagnostics."},{"key":"ref_29","unstructured":"Moreno-Armendariz, M.A. (2019, September 10). Deep Green Diagnostics Video, Youtube. Available online: https:\/\/youtu.be\/OnOQ8g0cAfc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.envsoft.2017.02.004","article-title":"Automatic land cover classification of geo-tagged field photos by deep learning","volume":"91","author":"Xu","year":"2017","journal-title":"Environ. Modell. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.landurbplan.2018.08.020","article-title":"Measuring human perceptions of a large-scale urban region using machine learning","volume":"180","author":"Zhang","year":"2018","journal-title":"Landscape Urban Plann."},{"key":"ref_32","unstructured":"Bengio, Y. (2011, January 2). Deep learning of representations for unsupervised and transfer learning. Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop, Bellevue, WA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1109\/TCYB.2017.2668395","article-title":"A Generic Deep-Learning-Based Approach for Automated Surface Inspection","volume":"48","author":"Ren","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Jaramillo, V., Fries, A., and Bendix, J. (2019). AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV). Remote Sens., 11.","DOI":"10.3390\/rs11121413"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xu, G., Zhu, X., Tapper, N., and Bechtel, B. (2019). Urban climate zone classification using convolutional neural network and ground-level images. Prog. Phys. Geogr. Earth Environ.","DOI":"10.1177\/0309133319837711"},{"key":"ref_36","unstructured":"Kitano, B.T., Mendes, C.C., Geus, A.R., Oliveira, H.C., and Souza, J.R. (2019). Corn Plant Counting Using Deep Learning and UAV Images. IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kinaneva, D., Hristov, G., Raychev, J., and Zahariev, P. (2019, January 20\u201324). Early Forest Fire Detection Using Drones and Artificial Intelligence. Proceedings of the 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2019.8756696"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1002\/rob.20113","article-title":"Terrain characterization and classification with a mobile robot","volume":"23","author":"Ojeda","year":"2006","journal-title":"J. Field Rob."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pellenz, J., Lang, D., Neuhaus, F., and Paulus, D. (2010, January 26\u201330). Real-time 3d mapping of rough terrain: A field report from disaster city. Proceedings of the 2010 IEEE Safety Security and Rescue Robotics, Bremen, Germany.","DOI":"10.1109\/SSRR.2010.5981567"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1109\/LGRS.2009.2013875","article-title":"Automatic detection of terrain surface changes after Wenchuan earthquake, May 2008, from ALOS SAR images using 2EM-MRF method","volume":"6","author":"Jin","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.ecolind.2007.05.005","article-title":"Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators","volume":"8","author":"Sumfleth","year":"2008","journal-title":"Ecol. Indic."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.landurbplan.2018.12.001","article-title":"Urban form and composition of street canyons: A human-centric big data and deep learning approach","volume":"183","author":"Middel","year":"2019","journal-title":"Landscape Urban Plann."},{"key":"ref_43","unstructured":"Atfarm (2019, September 10). Precise Fertilisation Made Simple. Available online: https:\/\/www.at.farm\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5287\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:39:00Z","timestamp":1760189940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/23\/5287"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,30]]},"references-count":43,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["s19235287"],"URL":"https:\/\/doi.org\/10.3390\/s19235287","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,11,30]]}}}