{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T23:00:05Z","timestamp":1769554805176,"version":"3.49.0"},"reference-count":17,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"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 interest in change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task; therefore, a standard approach with manual detection of the elements of interest by experts in the domain of Earth Observation needs to be replaced by innovative methods that can guarantee optimal results with unquestionable value and within reasonable time. In this paper, we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to address these particular needs, which can be further refined and used in post-processing workflows for a large variety of applications.<\/jats:p>","DOI":"10.3390\/rs13204083","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T21:48:39Z","timestamp":1634161719000},"page":"4083","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Deep Learning Approaches to Earth Observation Change Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9233-3632","authenticated-orcid":false,"given":"Antonio","family":"Di Pilato","sequence":"first","affiliation":[{"name":"Dipartimento Interateneo di Fisica, Universit\u00e0 Degli Studi di Bari Aldo Moro, 70126 Bari, Italy"}]},{"given":"Nicol\u00f2","family":"Taggio","sequence":"additional","affiliation":[{"name":"Planetek Italia s.r.l, 70132 Bari, Italy"}]},{"given":"Alexis","family":"Pompili","sequence":"additional","affiliation":[{"name":"Dipartimento Interateneo di Fisica, Universit\u00e0 Degli Studi di Bari Aldo Moro, 70126 Bari, Italy"}]},{"given":"Michele","family":"Iacobellis","sequence":"additional","affiliation":[{"name":"Planetek Italia s.r.l, 70132 Bari, Italy"}]},{"given":"Adriano","family":"Di Florio","sequence":"additional","affiliation":[{"name":"Dipartimento Interateneo di Fisica, Politecnico di Bari, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1928-1263","authenticated-orcid":false,"given":"Davide","family":"Passarelli","sequence":"additional","affiliation":[{"name":"Planetek Italia s.r.l, 70132 Bari, Italy"}]},{"given":"Sergio","family":"Samarelli","sequence":"additional","affiliation":[{"name":"Planetek Italia s.r.l, 70132 Bari, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review Article Digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6635","DOI":"10.1080\/01431161.2019.1583394","article-title":"Change detection in urban areas from remote sensing data: A multidimensional classification scheme","volume":"40","author":"Salah","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Papadomanolaki, M., Verma, S., Vakalopoulou, M., Gupta, S., and Karantzalos, K. (August, January 28). Detecting urban changes with recurrent neural networks from multitemporal Sentinel-2 data. Proceedings of the IGARSS 2019\u2014IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900330"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.006","article-title":"A critical synthesis of remotely sensed optical image change detection techniques","volume":"160","author":"Tewkesbury","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Panuju, D.R., Paull, D.J., and Griffin, A.L. (2020). Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics. Remote Sens., 12.","DOI":"10.3390\/rs12111781"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101310","DOI":"10.1016\/j.ecoinf.2021.101310","article-title":"Analysis on change detection techniques for remote sensing applications: A review","volume":"63","author":"Afaq","year":"2021","journal-title":"Ecol. Informatics"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ayhan, B., and Kwan, C. (2019, January 10\u201312). New Results in Change Detection Using Optical and Multispectral Images. Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON47517.2019.8992937"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kou, R., Fang, B., Chen, G., and Wang, L. (2020). Progressive Domain Adaptation for Change Detection Using Season-Varying Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12223815"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, M., Zhang, H., Sun, W., Li, S., Wang, F., and Yang, G. (2020). A Coarse-to-Fine Deep Learning Based Land Use Change Detection Method for High-Resolution Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12121933"},{"key":"ref_10","unstructured":"Cazaubiel, V., Chorvalli, V., and Miesch, C. (2008, January 14\u201317). The multispectral instrument of the Sentinel-2 program. Proceedings of the International Conference on Space Optics\u2014ICSO 2008, International Society for Optics and Photonics, Toulouse, France."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Daudt, R.C., Le Saux, B., Boulch, A., and Gousseau, Y. (2018, January 22\u201327). Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518015"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_13","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_14","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Sudre, C., Li, W., Vercauteren, T., Ourselin, S., and Cardoso, M.J. (2017). Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4083\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:12:26Z","timestamp":1760166746000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4083"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,13]]},"references-count":17,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204083"],"URL":"https:\/\/doi.org\/10.3390\/rs13204083","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,13]]}}}