{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T18:23:11Z","timestamp":1786818191840,"version":"3.56.0"},"reference-count":45,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T00:00:00Z","timestamp":1630540800000},"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>In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study, with a prototype of an on board Artificial Intelligence (AI) model, and realistic testing equipment and scenario are presented in this work. As a case study, the detection of volcanic eruptions has been investigated with the objective to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been designed and realized from scratch, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements. The CNNs are then tested with experimental hardware, by means of a drone with a paylod composed of a generic processing unit (Raspberry PI), an AI processing unit (Movidius stick) and a camera. The hardware employed to build the prototype is low-cost, easy to found and to use. Moreover, the dataset has been published on GitHub, made available to everyone. The results are promising and encouraging toward the employment of the proposed system in future missions, given that ESA has already moved the first steps of AI on board with the Phisat-1 satellite, launched on September 2020.<\/jats:p>","DOI":"10.3390\/rs13173479","type":"journal-article","created":{"date-parts":[[2021,9,2]],"date-time":"2021-09-02T23:05:12Z","timestamp":1630623912000},"page":"3479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["On-Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"Maria Pia","family":"Del Rosso","sequence":"first","affiliation":[{"name":"Engineering Department, University of Sannio, 82100 Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9252-907X","authenticated-orcid":false,"given":"Alessandro","family":"Sebastianelli","sequence":"additional","affiliation":[{"name":"Engineering Department, University of Sannio, 82100 Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6877-3187","authenticated-orcid":false,"given":"Dario","family":"Spiller","sequence":"additional","affiliation":[{"name":"\u03a6-Lab, ASI-ESA, 00044 Frascati, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre Philippe","family":"Mathieu","sequence":"additional","affiliation":[{"name":"\u03a6-Lab, ASI-ESA, 00044 Frascati, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6294-0581","authenticated-orcid":false,"given":"Silvia Liberata","family":"Ullo","sequence":"additional","affiliation":[{"name":"Engineering Department, University of Sannio, 82100 Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124905","DOI":"10.1016\/j.jhydrol.2020.124905","article-title":"A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses","volume":"586","author":"Karthikeyan","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_2","first-page":"1","article-title":"An appraisal on the progress of remote sensing applications in soil erosion mapping and monitoring","volume":"9","author":"Sepuru","year":"2018","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/10095020.2017.1337317","article-title":"A review of remote sensing applications for oil palm studies","volume":"20","author":"Chong","year":"2017","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ding, L., Ma, L., Li, L., Liu, C., Li, N., Yang, Z., Yao, Y., and Lu, H. (2021). A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods. Remote Sens., 13.","DOI":"10.3390\/rs13091818"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, J., Qu, C., and Shao, J. (2017, January 13\u201314). Ship detection in SAR images based on an improved faster R-CNN. Proceedings of the 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China.","DOI":"10.1109\/BIGSARDATA.2017.8124934"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Ke, X., Liu, C., Xu, X., Zhan, X., Wang, C., Ahmad, I., Zhou, Y., and Pan, D. (2021). HOG-ShipCLSNet: A Novel Deep Learning Network with HOG Feature Fusion for SAR Ship Classification. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2021.3082759"},{"key":"ref_7","unstructured":"Bentes, C., Frost, A., Velotto, D., and Tings, B. (2016, January 6\u20139). Ship-iceberg discrimination with convolutional neural networks in high resolution SAR images. Proceedings of the EUSAR 2016: 11th European Conference on Synthetic Aperture Radar, Hamburg, Germany."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"69742","DOI":"10.1109\/ACCESS.2020.2985637","article-title":"Efficient low-cost ship detection for SAR imagery based on simplified U-net","volume":"8","author":"Mao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mao, Y., Li, X., Su, H., Zhou, Y., and Li, J. (2020, January 11\u201313). Ship Detection for SAR Imagery Based on Deep Learning: A Benchmark. Proceedings of the 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC49862.2020.9339055"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1109\/JSTARS.2021.3064981","article-title":"A New Mask R-CNN-Based Method for Improved Landslide Detection","volume":"14","author":"Ullo","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ullo, S.L., Langenkamp, M.S., Oikarinen, T.P., DelRosso, M.P., Sebastianelli, A., and Sica, S. (August, January 28). Landslide geohazard assessment with convolutional neural networks using sentinel-2 imagery data. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898632"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Giuffrida, G., Diana, L., de Gioia, F., Benelli, G., Meoni, G., Donati, M., and Fanucci, L. (2020). CloudScout: A Deep Neural Network for On-Board Cloud Detection on Hyperspectral Images. Remote Sens., 12.","DOI":"10.3390\/rs12142205"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2020.01.085","article-title":"Recent advances in deep learning for object detection","volume":"396","author":"Wu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_14","first-page":"760","article-title":"In-orbit demonstration of the first hyperspectral imager for nanosatellites","volume":"Volume 11180","author":"Sodnik","year":"2019","journal-title":"International Conference on Space Optics\u2014ICSO 2018"},{"key":"ref_15","first-page":"88","article-title":"In-orbit demonstration of artificial intelligence applied to hyperspectral and thermal sensing from space","volume":"Volume 11131","author":"Pagano","year":"2019","journal-title":"CubeSats and SmallSats for Remote Sensing III"},{"key":"ref_16","unstructured":"European Space Agency (ESA) (2021, June 24). \u03a6-sat Artificial Intelligence for Earth Observation. Available online: https:\/\/www.esa.int\/Applications\/Observing_the_Earth\/Ph-sat."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, P., Li, Q., Zhang, B., Wu, F., Zhao, K., Du, X., Yang, C., and Zhong, R. (2021). On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13101995"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Del Rosso, M.P., Sebastianelli, A., and Ullo, S.L. (2021). Artificial Intelligence Applied to Satellite-Based Remote Sensing Data for Earth Observation, The Institution of Engineering and Technology (IET).","DOI":"10.1049\/PBTE098E"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/JSTARS.2018.2803198","article-title":"A deep neural networks approach to automatic recognition systems for volcano-seismic events","volume":"11","author":"Titos","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","unstructured":"Anantrasirichai, N., Albino, F., Hill, P., Bull, D., and Biggs, J. (2018). Detecting Volcano Deformation in InSAR using Deep learning. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111179","DOI":"10.1016\/j.rse.2019.04.032","article-title":"A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets","volume":"230","author":"Anantrasirichai","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e2020JB019840","DOI":"10.1029\/2020JB019840","article-title":"Automatic Detection of Volcanic Surface Deformation Using Deep Learning","volume":"125","author":"Sun","year":"2020","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Valade, S., Ley, A., Massimetti, F., D\u2019Hondt, O., Laiolo, M., Coppola, D., Loibl, D., Hellwich, O., and Walter, T.R. (2019). Towards global volcano monitoring using multisensor sentinel missions and artificial intelligence: The MOUNTS monitoring system. Remote Sens., 11.","DOI":"10.3390\/rs11131528"},{"key":"ref_24","first-page":"GC029-03","article-title":"Analysis of first PRISMA acquisitions on volcanoes and geothermal areas in Italy; comparisons with model simulations, past Hyperion data and field campaigns","volume":"2020","author":"Buongiorno","year":"2020","journal-title":"AGU Fall Meet. Abstr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jvolgeores.2003.11.008","article-title":"Sulfur dioxide flux estimation from volcanoes using advanced spaceborne thermal emission and reflection radiometer\u2014A case study of Miyakejima volcano, Japan","volume":"134","author":"Urai","year":"2004","journal-title":"J. Volcanol. Geotherm. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Colini, L., Spinetti, C., Doumaz, F., Amici, S., Ananasso, C., Buongiorno, M.F., Cafaro, P., Caltabiano, T., Curci, G., and D\u2019Andrea, S. (2013, January 21\u201326). 2012 hyperspectral airborne campaign on Etna: Multi data acquisition for ASI-PRISMA project. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723817"},{"key":"ref_27","unstructured":"(2021, June 24). Volcanoes of the World, Smithsonian Institution, National Museum of Natural History, Global Volcanism Program. Available online: http:\/\/volcano.si.edu\/database\/search_eruption_results.cfm."},{"key":"ref_28","first-page":"100739","article-title":"Automatic Dataset Builder for Machine Learning Applications to Satellite Imagery","volume":"15","author":"Sebastianelli","year":"2021","journal-title":"Elsevier Softw.-X"},{"key":"ref_29","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_30","unstructured":"Raspberry Pi Foundation (2021, July 17). Raspberry Pi. Available online: https:\/\/www.raspberrypi.org\/."},{"key":"ref_31","unstructured":"Sentinel Hub Blog (2021, July 17). Active Volcanoes as Seen from Space. Available online: https:\/\/medium.com\/sentinel-hub\/active-volcanoes-as-seen-from-space-9d1de0133733."},{"key":"ref_32","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_33","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_34","unstructured":"Del Rosso, M.P., Sebastianelli, A., Spiller, D., Mathieu, P.P., and Ullo, S.L. (2021, August 25). On Board Volcanic Eruption Detection Git-Hub Repository. Available online: https:\/\/github.com\/Sebbyraft\/OnBoardVolcanicEruptionDetection."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kim, P. (2017). Convolutional neural network. MATLAB Deep Learning, Apress.","DOI":"10.1007\/978-1-4842-2845-6"},{"key":"ref_36","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","unstructured":"LeCun, Y. (2021, August 20). LeNet-5, Convolutional Neural Networks. Available online: http:\/\/yann.Lecun.Com\/exdb\/lenet."},{"key":"ref_38","unstructured":"ESA \u03a6-Lab (2021, June 24). AI4EO Git-Hub Page. Available online: https:\/\/github.com\/ESA-PhiLab\/ai4eo."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mohammed, R., Rawashdeh, J., and Abdullah, M. (2020, January 7\u20139). Machine learning with oversampling and undersampling techniques: Overview study and experimental results. Proceedings of the 2020 11th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan.","DOI":"10.1109\/ICICS49469.2020.239556"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1109\/LGRS.2020.2993899","article-title":"ShipDeNet-20: An only 20 convolution layers and <1-MB lightweight SAR ship detector","volume":"18","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","unstructured":"Intel (2021, July 17). Neural Compute Stick. Available online: https:\/\/software.intel.com\/content\/dam\/develop\/public\/us\/en\/documents\/ncs2-data-sheet.pdf."},{"key":"ref_42","unstructured":"RaspberryPI (2021, July 17). RaspberryPI Camera Module Datasheet. Available online: https:\/\/www.raspberrypi.org\/documentation\/hardware\/camera\/."},{"key":"ref_43","unstructured":"RaspberryPI (2021, July 17). RaspberryPI Datasheet. Available online: https:\/\/datasheets.raspberrypi.org\/bcm2711\/bcm2711-peripherals.pdf."},{"key":"ref_44","unstructured":"OpenVINO (2021, July 17). OpenVINO Toolkit Website. Available online: https:\/\/docs.openvinotoolkit.org\/latest\/index.html."},{"key":"ref_45","unstructured":"European Space Agency (ESA) (2021, June 24). \u03a6-Lab. Available online: https:\/\/philab.phi.esa.int\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3479\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:54:46Z","timestamp":1760165686000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/17\/3479"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,2]]},"references-count":45,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13173479"],"URL":"https:\/\/doi.org\/10.3390\/rs13173479","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,2]]}}}