{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:48:34Z","timestamp":1775738914486,"version":"3.50.1"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031713965","type":"print"},{"value":"9783031713972","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-71397-2_22","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T12:02:50Z","timestamp":1729771370000},"page":"349-363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multimodal Earth Observation Modeling Using AI"],"prefix":"10.1007","author":[{"given":"Mirko Paolo","family":"Barbato","sequence":"first","affiliation":[]},{"given":"Flavio","family":"Piccoli","sequence":"additional","affiliation":[]},{"given":"Paolo","family":"Napoletano","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"22_CR1","unstructured":"Copernicus. scihub-copernicus. https:\/\/scihub.copernicus.eu\/"},{"key":"22_CR2","unstructured":"DSTL Satellite Imagery Feature Detection. Kaggle. https:\/\/www.kaggle.com\/c\/dstl-satellite-imagery-feature-detection"},{"key":"22_CR3","unstructured":"SAGA-GIS. SAGA-GIS-Software. https:\/\/saga-gis.sourceforge.io\/"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"ISPRS 2D Semantic Labeling (2018). Isprs http:\/\/www2.isprs.org\/commissions\/comm3\/wg4\/semantic-labeling.html","DOI":"10.5194\/isprs-annals-IV-2-W5-3-2019"},{"key":"22_CR5","unstructured":"Arora, S.K.: Spacenet information (2018). Medium https:\/\/sumit-arora.medium.com\/getting-started-with-aws-spacenet-and-spacenet-dataset-visualization-basics-7ddd2e5809a2"},{"key":"22_CR6","volume":"28","author":"MP Barbato","year":"2022","unstructured":"Barbato, M.P., Napoletano, P., Piccoli, F., Schettini, R.: Unsupervised segmentation of hyperspectral remote sensing images with superpixels. Remote Sens. Appl. Soc. Environ. 28, 100823 (2022)","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Barbato, M.P., Piccoli, F., Napoletano, P.: Ticino: a multi-modal remote sensing dataset for semantic segmentation. Available at SSRN 4535928 (2023)","DOI":"10.2139\/ssrn.4535928"},{"issue":"1","key":"22_CR8","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"2","key":"22_CR9","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2), 125 (2020)","journal-title":"Information"},{"issue":"12","key":"22_CR10","doi-asserted-by":"publisher","first-page":"4208","DOI":"10.3390\/s21124208","volume":"21","author":"O Chambers","year":"2021","unstructured":"Chambers, O., et al.: Machine learning strategy for soil nutrients prediction using spectroscopic method. Sensors 21(12), 4208 (2021)","journal-title":"Sensors"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Demir, I., et al.: DeepGlobe 2018: a challenge to parse the earth through satellite images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 172\u2013181 (2018)","DOI":"10.1109\/CVPRW.2018.00031"},{"issue":"12","key":"22_CR12","doi-asserted-by":"publisher","first-page":"4632","DOI":"10.1109\/JSTARS.2014.2341175","volume":"7","author":"JA dos Santos","year":"2014","unstructured":"dos Santos, J.A., et al.: Efficient and effective hierarchical feature propagation. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(12), 4632\u20134643 (2014)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"22_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0170478","volume":"12","author":"G Forkuor","year":"2017","unstructured":"Forkuor, G., Hounkpatin, O.K.L., Welp, G., Thiel, M.: High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. PLoS ONE 12, 1\u201321 (2017)","journal-title":"PLoS ONE"},{"key":"22_CR14","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.geoderma.2018.09.003","volume":"337","author":"L Guo","year":"2019","unstructured":"Guo, L., Zhang, H., Shi, T., Chen, Y., Jiang, Q., Linderman, M.: Prediction of soil organic carbon stock by laboratory spectral data and airborne hyperspectral images. Geoderma 337, 32\u201341 (2019)","journal-title":"Geoderma"},{"key":"22_CR15","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.isprsjprs.2023.05.032","volume":"202","author":"W Han","year":"2023","unstructured":"Han, W., et al.: A survey of machine learning and deep learning in remote sensing of geological environment: challenges, advances, and opportunities. ISPRS J. Photogramm. Remote. Sens. 202, 87\u2013113 (2023)","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"22_CR16","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1109\/TIP.2023.3245324","volume":"32","author":"Q He","year":"2023","unstructured":"He, Q., Sun, X., Diao, W., Yan, Z., Yao, F., Kun, F.: Multimodal remote sensing image segmentation with intuition-inspired hypergraph modeling. IEEE Trans. Image Process. 32, 1474\u20131487 (2023)","journal-title":"IEEE Trans. Image Process."},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Howard, A., et\u00a0al.: Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"7","key":"22_CR18","doi-asserted-by":"publisher","first-page":"736","DOI":"10.3390\/rs11070736","volume":"11","author":"H Jie","year":"2019","unstructured":"Jie, H., et al.: Quantitative estimation of soil salinity using UAV-borne hyperspectral and satellite multispectral images. Remote Sens. 11(7), 736 (2019)","journal-title":"Remote Sens."},{"key":"22_CR19","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1007\/s11119-009-9123-3","volume":"11","author":"M Ladoni","year":"2009","unstructured":"Ladoni, M., Bahrami, H., Panah, S.K.A., Norouzi, A.: Estimating soil organic carbon from soil reflectance: a review. Precis. Agric. 11, 82\u201399 (2009)","journal-title":"Precis. Agric."},{"key":"22_CR20","volume":"112","author":"J Li","year":"2022","unstructured":"Li, J., et al.: Deep learning in multimodal remote sensing data fusion: a comprehensive review. Int. J. Appl. Earth Obs. Geoinf. 112, 102926 (2022)","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"issue":"21","key":"22_CR21","doi-asserted-by":"publisher","first-page":"6271","DOI":"10.3390\/s20216271","volume":"20","author":"R Li","year":"2020","unstructured":"Li, R., Yin, B., Cong, Y., Zehua, D.: Simultaneous prediction of soil properties using multi_cnn model. Sensors 20(21), 6271 (2020)","journal-title":"Sensors"},{"issue":"3","key":"22_CR22","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/MGRS.2015.2440094","volume":"3","author":"L Loncan","year":"2015","unstructured":"Loncan, L., et al.: Hyperspectral pansharpening: a review. IEEE Geosci. Remote Sens. Mag. 3(3), 27\u201346 (2015)","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"22_CR23","unstructured":"Ayerdi, B., Gra\u00f1a, M., Veganzons, M.A.: Hyperspectral Remote Sensing Scenes (2020). Ehu http:\/\/www.ehu.eus\/ccwintco\/index.php\/Hyperspectral_Remote_Sensing_Scenes"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P.: Can semantic labeling methods generalize to any city? The Inria aerial image labeling benchmark. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 3226\u20133229. IEEE (2017)","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"22_CR25","volume":"89","author":"X Meng","year":"2020","unstructured":"Meng, X., et al.: Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. Int. J. Appl. Earth Obs. Geoinf. 89, 102111 (2020)","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"22_CR26","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1016\/j.neucom.2022.01.005","volume":"493","author":"Y Mo","year":"2022","unstructured":"Mo, Y., Yan, W., Yang, X., Liu, F., Liao, Y.: Review the state-of-the-art technologies of semantic segmentation based on deep learning. Neurocomputing 493, 626\u2013646 (2022)","journal-title":"Neurocomputing"},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"Mohanty, S.P., et\u00a0al.: Deep learning for understanding satellite imagery: an experimental survey. Front. Artif. Intell. 3 (2020)","DOI":"10.3389\/frai.2020.534696"},{"key":"22_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.geoderma.2010.12.018","volume":"162","author":"VL Mulder","year":"2011","unstructured":"Mulder, V.L., de Bruin, S., Schaepman, M., Mayr, T.R.: The use of remote sensing in soil and terrain mapping - a review. Geoderma 162, 1\u201319 (2011)","journal-title":"Geoderma"},{"issue":"2","key":"22_CR29","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.landusepol.2011.07.003","volume":"29","author":"P Panagos","year":"2012","unstructured":"Panagos, P., Van Liedekerke, M., Jones, A., Montanarella, L.: European soil data centre: response to European policy support and public data requirements. Land Use Policy 29(2), 329\u2013338 (2012)","journal-title":"Land Use Policy"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Piccoli, F., Barbato, M.P., Peracchi, M., Napoletano, P.: Estimation of soil characteristics from multispectral sentinel-3 imagery and dem derivatives using machine learning. Sensors 23(18) (2023)","DOI":"10.3390\/s23187876"},{"key":"22_CR31","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.3390\/rs12091369","volume":"12","author":"J Safanelli","year":"2020","unstructured":"Safanelli, J., Chabrillat, S., Ben-Dor, E., Dematt\u00ea, J.: Multispectral models from bare soil composites for mapping topsoil properties over Europe. Remote Sens. 12, 1369 (2020)","journal-title":"Remote Sens."},{"key":"22_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106669","volume":"126","author":"H Thisanke","year":"2023","unstructured":"Thisanke, H., Deshan, C., Chamith, K., Seneviratne, S., Vidanaarachchi, R., Herath, D.: Semantic segmentation using vision transformers: a survey. Eng. Appl. Artif. Intell. 126, 106669 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1\u20132","key":"22_CR33","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/S0016-7061(00)00081-1","volume":"100","author":"JA Thompson","year":"2001","unstructured":"Thompson, J.A., Bell, J.C., Butler, C.A.: Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling. Geoderma 100(1\u20132), 67\u201389 (2001)","journal-title":"Geoderma"},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"T\u00e9th, G., Jones, A., Montanarella, L.: The Lucas topsoil database and derived information on the regional variability of cropland topsoil properties in the European union. Environ. Monit. Assess. 185 (2013)","DOI":"10.1007\/s10661-013-3109-3"},{"key":"22_CR35","unstructured":"Van\u00a0Etten, A., Lindenbaum, D., Bacastow, T.M.: SpaceNet: a remote sensing dataset and challenge series. arXiv preprint arXiv:1807.01232 (2018)"},{"key":"22_CR36","volume-title":"The Nature of Statistical Learning Theory","author":"V Vapnik","year":"2013","unstructured":"Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Cham (2013)"},{"key":"22_CR37","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1109\/JSTARS.2022.3220974","volume":"16","author":"G Vivone","year":"2022","unstructured":"Vivone, G., Garzelli, A., Yang, X., Liao, W., Chanussot, J.: Panchromatic and hyperspectral image fusion: outcome of the 2022 whispers hyperspectral pansharpening challenge. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16, 166\u2013179 (2022)","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"22_CR38","doi-asserted-by":"crossref","unstructured":"Volpi, M., Ferrari, V.: Semantic segmentation of urban scenes by learning local class interactions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPRW.2015.7301377"},{"key":"22_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114417","volume":"169","author":"X Yuan","year":"2021","unstructured":"Yuan, X., Shi, J., Lichuan, G.: A review of deep learning methods for semantic segmentation of remote sensing imagery. Expert Syst. Appl. 169, 114417 (2021)","journal-title":"Expert Syst. Appl."},{"key":"22_CR40","doi-asserted-by":"crossref","unstructured":"Zhou, T., et al.: Prediction of soil organic carbon and the c:n ratio on a national scale using machine learning and satellite data: a comparison between sentinel-2, sentinel-3 and landsat-8 images. Sci. Total Environ. 142661 (2021)","DOI":"10.1016\/j.scitotenv.2020.142661"}],"container-title":["Lecture Notes in Computer Science","Modelling and Simulation for Autonomous Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-71397-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T12:05:35Z","timestamp":1729771535000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-71397-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031713965","9783031713972"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-71397-2_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MESAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Modelling and Simulation for Autonomous Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Palermo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mesas2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.mscoe.org\/event\/mesas-2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}