{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:40:12Z","timestamp":1775284812353,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T00:00:00Z","timestamp":1628553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.<\/jats:p>","DOI":"10.3390\/rs13163166","type":"journal-article","created":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T22:40:31Z","timestamp":1628635231000},"page":"3166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Jash R.","family":"Parekh","sequence":"first","affiliation":[{"name":"Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA"}]},{"given":"Ate","family":"Poortinga","sequence":"additional","affiliation":[{"name":"Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA"},{"name":"SERVIR-Mekong, SM Tower, 24th Floor, 979\/69 Paholyothin Road, Samsen Nai Phayathai, Bangkok 10400, Thailand"}]},{"given":"Biplov","family":"Bhandari","sequence":"additional","affiliation":[{"name":"SERVIR-Science Coordination Office, NASA Marshall Space Flight Center, 320 Sparkman Dr., Huntsville, AL 35805, USA"},{"name":"Department of Atmospheric and Earth Science, The University of Alabama in Huntsville, 320 Sparkman Dr., Huntsville, AL 35805, USA"}]},{"given":"Timothy","family":"Mayer","sequence":"additional","affiliation":[{"name":"SERVIR-Science Coordination Office, NASA Marshall Space Flight Center, 320 Sparkman Dr., Huntsville, AL 35805, USA"},{"name":"Earth System Science Center, The University of Alabama in Huntsville, 320 Sparkman Dr., Huntsville, AL 35805, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9999-1219","authenticated-orcid":false,"given":"David","family":"Saah","sequence":"additional","affiliation":[{"name":"Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA"},{"name":"SERVIR-Mekong, SM Tower, 24th Floor, 979\/69 Paholyothin Road, Samsen Nai Phayathai, Bangkok 10400, Thailand"},{"name":"Geospatial Analysis Lab, University of San Francisco, 2130 Fulton St., San Francisco, CA 94117, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6392-6084","authenticated-orcid":false,"given":"Farrukh","family":"Chishtie","sequence":"additional","affiliation":[{"name":"Spatial Informatics Group, LLC, 2529 Yolanda Ct., Pleasanton, CA 94566, USA"},{"name":"SERVIR-Mekong, SM Tower, 24th Floor, 979\/69 Paholyothin Road, Samsen Nai Phayathai, Bangkok 10400, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,10]]},"reference":[{"key":"ref_1","unstructured":"United Nations Department of Economic and Social Affairs (UN DESA) (2021, May 01). World\u2019s Population Increasingly Urban with more than Half Living in Urban Areas. Available online: https:\/\/www.un.org\/en\/development\/desa\/news\/population\/world-urbanization-prospects-2014.html."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1177\/088541202400903563","article-title":"Impervious surfaces and water quality: A review of current literature and its implications for watershed planning","volume":"16","author":"Brabec","year":"2002","journal-title":"J. Plan. Lit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1073\/pnas.0911131107","article-title":"Housing growth in and near United States protected areas limits their conservation value","volume":"107","author":"Radeloff","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.resconrec.2018.10.011","article-title":"Biodiversity and the built environment: Implications for the Sustainable Development Goals (SDGs)","volume":"141","author":"Opoku","year":"2019","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/joc.859","article-title":"Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island","volume":"23","author":"Arnfield","year":"2003","journal-title":"Int. J. Climatol. A J. R. Meteorol. Soc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0921-8181(00)00021-7","article-title":"The impact of land use\u2014land cover changes due to urbanization on surface microclimate and hydrology: A satellite perspective","volume":"25","author":"Carlson","year":"2000","journal-title":"Glob. Planet. Chang."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Seto, K.C., Fragkias, M., G\u00fcneralp, B., and Reilly, M.K. (2011). A meta-analysis of global urban land expansion. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0023777"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Seto, K.C., Parnell, S., and Elmqvist, T. (2013). A global outlook on urbanization. Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities, Springer.","DOI":"10.1007\/978-94-007-7088-1_1"},{"key":"ref_9","unstructured":"World Health Organization (2021, May 01). The World Health Report, Life in the 21st Century, A Vision for All, Report of the Director-General. Available online: https:\/\/apps.who.int\/iris\/handle\/10665\/42065."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1078\/1438-4639-00223","article-title":"Global urbanization and impact on health","volume":"206","author":"Moore","year":"2003","journal-title":"Int. J. Hyg. Environ. Health"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Madhumathi, A., Subhashini, S., and VishnuPriya, J. (2018). The Urban Heat Island Effect its Causes and Mitigation with Reference to the Thermal Properties of Roof Coverings. Proceedings of the International Conference on Urban Sustainability: Emerging Trends Themes, Concepts & Practices (ICUS), Jaipur, India, 16\u201318 March 2018, Malaviya National Institute of Techonology.","DOI":"10.2139\/ssrn.3207224"},{"key":"ref_12","unstructured":"Polycarpou, L. (2010). No More Pavement! The Problem of Impervious Surfaces, State of the Planet, Columbia University Earth Institute."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Frazer, L. (2005). Paving Paradise: The Peril of Impervious Surfaces, National Institute of Environmental Health Sciences.","DOI":"10.1289\/ehp.113-a456"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.3390\/s7123209","article-title":"Remote sensing sensors and applications in environmental resources mapping and modelling","volume":"7","author":"Melesse","year":"2007","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1007\/s12665-012-1918-2","article-title":"Impervious surface impact on water quality in the process of rapid urbanization in Shenzhen, China","volume":"68","author":"Liu","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kim, H., Jeong, H., Jeon, J., and Bae, S. (2016). The impact of impervious surface on water quality and its threshold in Korea. Water, 8.","DOI":"10.3390\/w8040111"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/01944369608975688","article-title":"Impervious surface coverage: The emergence of a key environmental indicator","volume":"62","author":"Arnold","year":"1996","journal-title":"J. Am. Plan. Assoc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","first-page":"101979","article-title":"Primitives as building blocks for constructing land cover maps","volume":"85","author":"Saah","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111278","DOI":"10.1016\/j.rse.2019.111278","article-title":"Annual continuous fields of woody vegetation structure in the Lower Mekong region from 2000\u20132017 Landsat time-series","volume":"232","author":"Potapov","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Poortinga, A., Tenneson, K., Shapiro, A., Nquyen, Q., San Aung, K., Chishtie, F., and Saah, D. (2019). Mapping plantations in Myanmar by fusing Landsat-8, Sentinel-2 and Sentinel-1 data along with systematic error quantification. Remote Sens., 11.","DOI":"10.3390\/rs11070831"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"044003","DOI":"10.1088\/1748-9326\/4\/4\/044003","article-title":"A new map of global urban extent from MODIS satellite data","volume":"4","author":"Schneider","year":"2009","journal-title":"Environ. Res. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"150","DOI":"10.3389\/fenvs.2019.00150","article-title":"Land cover mapping in data scarce environments: Challenges and opportunities","volume":"7","author":"Saah","year":"2019","journal-title":"Front. Environ. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Markert, K.N., Markert, A.M., Mayer, T., Nauman, C., Haag, A., Poortinga, A., Bhandari, B., Thwal, N.S., Kunlamai, T., and Chishtie, F. (2020). Comparing sentinel-1 surface water mapping algorithms and radiometric terrain correction processing in southeast asia utilizing google earth engine. Remote Sens., 12.","DOI":"10.3390\/rs12152469"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Poortinga, A., Aekakkararungroj, A., Kityuttachai, K., Nguyen, Q., Bhandari, B., Soe Thwal, N., Priestley, H., Kim, J., Tenneson, K., and Chishtie, F. (2020). Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region. Remote Sens., 12.","DOI":"10.3390\/rs12091472"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"191","DOI":"10.3389\/fenvs.2019.00191","article-title":"Operational flood risk index mapping for disaster risk reduction using Earth Observations and cloud computing technologies: A case study on Myanmar","volume":"7","author":"Phongsapan","year":"2019","journal-title":"Front. Environ. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Poortinga, A., Clinton, N., Saah, D., Cutter, P., Chishtie, F., Markert, K.N., Anderson, E.R., Troy, A., Fenn, M., and Tran, L.H. (2018). An operational before-after-control-impact (BACI) designed platform for vegetation monitoring at planetary scale. Remote Sens., 10.","DOI":"10.3390\/rs10050760"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1080\/02757250109532436","article-title":"Remote sensing of impervious surfaces: A review","volume":"20","author":"Slonecker","year":"2001","journal-title":"Remote Sens. Rev."},{"key":"ref_29","unstructured":"Bauer, M.E., Heinert, N.J., Doyle, J.K., and Yuan, F. (2004). Impervious surface mapping and change monitoring using Landsat remote sensing. ASPRS Annual Conference Proceedings, American Society for Photogrammetry and Remote Sensing Bethesda."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Khanal, N., Matin, M.A., Uddin, K., Poortinga, A., Chishtie, F., Tenneson, K., and Saah, D. (2020). A comparison of three temporal smoothing algorithms to improve land cover classification: A case study from NEPAL. Remote Sens., 12.","DOI":"10.3390\/rs12182888"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2007). Remote Sensing of Impervious Surfaces, CRC Press.","DOI":"10.1201\/9781420043754.fmatt"},{"key":"ref_32","first-page":"1143","article-title":"Remote sensing of impervious surface and its applications: A review","volume":"29","author":"Liu","year":"2010","journal-title":"Prog. Geogr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MGRS.2019.2927260","article-title":"Urban impervious surface detection from remote sensing images: A review of the methods and challenges","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pettorelli, N. (2013). The Normalized Difference Vegetation Index, Oxford University Press.","DOI":"10.1093\/acprof:osobl\/9780199693160.001.0001"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of normalized difference built-up index in automatically mapping urban areas from TM imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"557","DOI":"10.14358\/PERS.76.5.557","article-title":"Analysis of impervious surface and its impact on urban heat environment using the Normalized Difference Impervious Surface Index (NDISI)","volume":"76","author":"Xu","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1080\/2150704X.2013.798710","article-title":"MNDISI: A multi-source composition index for impervious surface area estimation at the individual city scale","volume":"4","author":"Liu","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2012.09.009","article-title":"BCI: A biophysical composition index for remote sensing of urban environments","volume":"127","author":"Deng","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tian, Y., Chen, H., Song, Q., and Zheng, K. (2018). A novel index for impervious surface area mapping: Development and validation. Remote Sens., 10.","DOI":"10.3390\/rs10101521"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3473","DOI":"10.1080\/014311600750037507","article-title":"Dynamics of urban growth in the Washington DC metropolitan area, 1973\u20131996, from Landsat observations","volume":"21","author":"Masek","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Shi, L., Ling, F., Ge, Y., Foody, G.M., Li, X., Wang, L., Zhang, Y., and Du, Y. (2017). Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: A case study in Wuhan City using Landsat time-series datasets from 1987 to 2016. Remote Sens., 9.","DOI":"10.3390\/rs9111148"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1016\/j.rse.2009.05.014","article-title":"Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.rse.2013.10.028","article-title":"Improving the impervious surface estimation with combined use of optical and SAR remote sensing images","volume":"141","author":"Zhang","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bauer, M.E., Loffelholz, B.C., and Wilson, B. (2008). Estimating and mapping impervious surface area by regression analysis of Landsat imagery. Remote Sensing of Impervious Surfaces, CRC Press.","DOI":"10.1201\/9781420043754.pt1"},{"key":"ref_45","first-page":"2014","article-title":"Open street map","volume":"18","author":"Map","year":"2014","journal-title":"Retr. March"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/15481603.2017.1417690","article-title":"Improving impervious surface estimation: An integrated method of classification and regression trees (CART) and linear spectral mixture analysis (LSMA) based on error analysis","volume":"55","author":"Wang","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Curlander, J.C., and McDonough, R.N. (1991). Synthetic Aperture Radar, Wiley.","DOI":"10.1016\/0045-8732(91)90094-O"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.rse.2015.07.017","article-title":"A Linear Dirichlet Mixture Model for decomposing scenes: Application to analyzing urban functional zonings","volume":"169","author":"Zhang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1007\/s11831-019-09344-w","article-title":"A survey of deep learning and its applications: A new paradigm to machine learning","volume":"27","author":"Dargan","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_52","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 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2010). Computer Vision: Algorithms and Applications, Springer Science & Business Media.","DOI":"10.1007\/978-1-84882-935-0"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., and Terzopoulos, D. (2020). Image segmentation using deep learning: A survey. arXiv.","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"ref_55","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_56","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_58","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 8\u201310). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_60","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_61","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_62","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_63","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Tompson, J., Goroshin, R., Jain, A., LeCun, Y., and Bregler, C. (2015, January 7\u201312). Efficient object localization using convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1162\/neco.1996.8.3.643","article-title":"The effects of adding noise during backpropagation training on a generalization performance","volume":"8","author":"An","year":"1996","journal-title":"Neural Comput."},{"key":"ref_66","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_67","unstructured":"Wang, Q., Ma, Y., Zhao, K., and Tian, Y. (2020). A comprehensive survey of loss functions in machine learning. Ann. Data Sci., 1\u201326."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1137\/16M1080173","article-title":"Optimization methods for large-scale machine learning","volume":"60","author":"Bottou","year":"2018","journal-title":"Siam Rev."},{"key":"ref_69","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_70","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_72","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916), Savannah, GA, USA."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"61677","DOI":"10.1109\/ACCESS.2018.2874767","article-title":"Performance analysis of google colaboratory as a tool for accelerating deep learning applications","volume":"6","author":"Carneiro","year":"2018","journal-title":"IEEE Access"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3166\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:43:39Z","timestamp":1760165019000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3166"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,10]]},"references-count":73,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163166"],"URL":"https:\/\/doi.org\/10.3390\/rs13163166","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,10]]}}}