{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:36:52Z","timestamp":1774964212094,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2013NextGenerationEU","award":["ECS00000041-VITALITY-CUP D83C22000710005"],"award-info":[{"award-number":["ECS00000041-VITALITY-CUP D83C22000710005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the rapidly evolving field of remote sensing, Deep Learning (DL) techniques have become pivotal in interpreting and processing complex datasets. However, the increasing reliance on these algorithms necessitates a robust ethical framework to evaluate their trustworthiness. This paper introduces a comprehensive ethical framework designed to assess and quantify the trustworthiness of DL techniques in the context of remote sensing. We first define trustworthiness in DL as a multidimensional construct encompassing accuracy, reliability, transparency and explainability, fairness, and accountability. Our framework then operationalizes these dimensions through a set of quantifiable metrics, allowing for the systematic evaluation of DL models. To illustrate the applicability of our framework, we selected an existing case study in remote sensing, wherein we apply our ethical assessment to a DL model used for classification. Our results demonstrate the model\u2019s performance across different trustworthiness metrics, highlighting areas for ethical improvement. This paper not only contributes a novel framework for ethical analysis in the field of DL, but also provides a practical tool for developers and practitioners in remote sensing to ensure the responsible deployment of DL technologies. Through a dual approach that combines top-down international standards with bottom-up, context-specific considerations, our framework serves as a practical tool for ensuring responsible AI applications in remote sensing. Its application through a case study highlights its potential to influence policy-making and guide ethical AI development in this domain.<\/jats:p>","DOI":"10.3390\/rs16234529","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T09:18:32Z","timestamp":1733217512000},"page":"4529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Ethical Framework to Assess and Quantify the Trustworthiness of Artificial Intelligence Techniques: Application Case in Remote Sensing"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5523-7174","authenticated-orcid":false,"given":"Marina","family":"Paolanti","sequence":"first","affiliation":[{"name":"Department of Political Sciences, Communication and International Relations, University of Macerata, 62100 Macerata, Italy"}]},{"given":"Simona","family":"Tiribelli","sequence":"additional","affiliation":[{"name":"Department of Political Sciences, Communication and International Relations, University of Macerata, 62100 Macerata, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8429-0871","authenticated-orcid":false,"given":"Benedetta","family":"Giovanola","sequence":"additional","affiliation":[{"name":"Department of Political Sciences, Communication and International Relations, University of Macerata, 62100 Macerata, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5281-9200","authenticated-orcid":false,"given":"Adriano","family":"Mancini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering (DII), Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8893-9244","authenticated-orcid":false,"given":"Emanuele","family":"Frontoni","sequence":"additional","affiliation":[{"name":"Department of Political Sciences, Communication and International Relations, University of Macerata, 62100 Macerata, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9160-834X","authenticated-orcid":false,"given":"Roberto","family":"Pierdicca","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering Building and Architecture (DICEA), Universit\u00e0 Politecnica delle Marche, 60131 Ancona, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Workman, S., Rafique, M.U., Blanton, H., and Jacobs, N. (2022, January 18\u201324). Revisiting near\/remote sensing with geospatial attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00182"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, F., and Ma, C. (2022, January 18\u201324). Sparse and Complete Latent Organization for Geospatial Semantic Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00185"},{"key":"ref_3","unstructured":"Paganini, M., Petiteville, I., Ward, S., Dyke, G., Steventon, M., Harry, J., and Kerblat, F. (2018). Satellite earth observations in support of the sustainable development goals. The CEOS Earth Observation Handbook, ESA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.5194\/gi-11-195-2022","article-title":"GeoAI: A review of artificial intelligence approaches for the interpretation of complex geomatics data","volume":"11","author":"Pierdicca","year":"2022","journal-title":"Geosci. Instrum. Methods Data Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1080\/17538947.2023.2200041","article-title":"An ethics assessment list for geoinformation ecosystems: Revisiting the integrated geospatial information framework of the United Nations","volume":"6","author":"Calzati","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Robinson, C., Ortiz, A., Park, H., Lozano, N., Kaw, J.K., Sederholm, T., Dodhia, R., and Ferres, J.M.L. (2022, January 18\u201324). Fast building segmentation from satellite imagery and few local labels. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00152"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mar\u00ed, R., Facciolo, G., and Ehret, T. (2022, January 18\u201324). Sat-nerf: Learning multi-view satellite photogrammetry with transient objects and shadow modeling using rpc cameras. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00137"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Huang, X., Ren, L., Liu, C., Wang, Y., Yu, H., Schmitt, M., H\u00e4nsch, R., Sun, X., Huang, H., and Mayer, H. (2022, January 18\u201324). Urban Building Classification (UBC)\u2014A Dataset for Individual Building Detection and Classification from Satellite Imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00147"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gominski, D., Gouet-Brunet, V., and Chen, L. (2022, January 18\u201324). Cross-dataset Learning for Generalizable Land Use Scene Classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00144"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chappuis, C., Zermatten, V., Lobry, S., Le Saux, B., and Tuia, D. (2022, January 18\u201324). Prompt-RSVQA: Prompting visual context to a language model for remote sensing visual question answering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00143"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Petrocchi, E., Tiribelli, S., Paolanti, M., Giovanola, B., Frontoni, E., and Pierdicca, R. (2023, January 11\u201315). GeomEthics: Ethical Considerations About Using Artificial Intelligence in Geomatics. Proceedings of the International Conference on Image Analysis and Processing, Udine, Italy.","DOI":"10.1007\/978-3-031-51026-7_25"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100363","DOI":"10.1016\/j.patter.2021.100363","article-title":"Fairness and accountability of AI in disaster risk management: Opportunities and challenges","volume":"2","author":"Gevaert","year":"2021","journal-title":"Patterns"},{"key":"ref_13","unstructured":"Gevaert, C.M. (2022, January 13\u201316). Finding biases in geospatial datasets in the Global South\u2013are we missing vulnerable populations?. Proceedings of the 41st EARSeL Symposium 2022: Earth Observation for Environmental Monitoring, Paphos, Cyprus."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"20539517221138767","DOI":"10.1177\/20539517221138767","article-title":"AI ethics and data governance in the geospatial domain of Digital Earth","volume":"9","author":"Micheli","year":"2022","journal-title":"Big Data Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20539517231191527","DOI":"10.1177\/20539517231191527","article-title":"Chinese sociotechnical imaginaries of Earth observation: From sight to foresight","volume":"10","author":"Bennett","year":"2023","journal-title":"Big Data Soc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bischke, B., Helber, P., Folz, J., Borth, D., and Dengel, A. (2019, January 22\u201325). Multi-task learning for segmentation of building footprints with deep neural networks. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803050"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bischke, B., Helber, P., Borth, D., and Dengel, A. (2018, January 22\u201327). Segmentation of imbalanced classes in satellite imagery using adaptive uncertainty weighted class loss. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517836"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gram-Hansen, B.J., Helber, P., Varatharajan, I., Azam, F., Coca-Castro, A., Kopackova, V., and Bilinski, P. (2019, January 27\u201328). Mapping informal settlements in developing countries using machine learning and low resolution multi-spectral data. Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society, Honolulu, HI, USA.","DOI":"10.1145\/3306618.3314253"},{"key":"ref_19","unstructured":"Rudner, T.G., Ru\u00dfwurm, M., Fil, J., Pelich, R., Bischke, B., Kopa\u010dkov\u00e1, V., and Bili\u0144ski, P. (February, January 27). Multi3net: Segmenting flooded buildings via fusion of multiresolution, multisensor, and multitemporal satellite imagery. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/MGRS.2022.3145854","article-title":"Artificial intelligence for remote sensing data analysis: A review of challenges and opportunities","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s11207-021-01780-x","article-title":"Automatic detection of occulted hard X-ray flares using deep-learning methods","volume":"296","author":"Ishikawa","year":"2021","journal-title":"Sol. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","first-page":"4502615","article-title":"Geo-Intelligent Retrieval Framework Based on Machine Learning in the Cloud Environment: A Case Study of Soil Moisture Retrieval","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"18617","DOI":"10.1007\/s11356-022-23431-9","article-title":"Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms","volume":"30","author":"Tian","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","article-title":"Joint Deep Learning for land cover and land use classification","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1109\/JSTARS.2023.3243396","article-title":"A dual-branch deep learning architecture for multisensor and multitemporal remote sensing semantic segmentation","volume":"16","author":"Bergamasco","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Nadeem, A.A., Zha, Y., Shi, L., Ali, S., Wang, X., Zafar, Z., Afzal, Z., and Tariq, M.A.U.R. (2023). Spatial downscaling and gap-filling of SMAP soil moisture to high resolution using MODIS surface variables and machine learning approaches over ShanDian River Basin, China. Remote Sens., 15.","DOI":"10.3390\/rs15030812"},{"key":"ref_31","first-page":"102734","article-title":"A review and meta-analysis of generative adversarial networks and their applications in remote sensing","volume":"108","author":"Jozdani","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative adversarial networks: An overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"20539517241235871","DOI":"10.1177\/20539517241235871","article-title":"Prediction and explainability in AI: Striking a new balance?","volume":"11","author":"Raz","year":"2024","journal-title":"Big Data Soc."},{"key":"ref_34","unstructured":"Ala-Pietil\u00e4, P., Bonnet, Y., Bergmann, U., Bielikova, M., Bonefeld-Dahl, C., Bauer, W., Bouarfa, L., Chatila, R., Coeckelbergh, M., and Dignum, V. (2020). The Assessment List for Trustworthy Artificial Intelligence (ALTAI), European Commission."},{"key":"ref_35","unstructured":"UNESCO (2021). Preliminary Report on the First Draft of the Recommendation on the Ethics of Artificial Intelligence, United Nations Educational, Scientific and Cultural Organization."},{"key":"ref_36","unstructured":"UN-GGIM (2024, December 01). Integrated Geospatial Information Framework (IGIF). Available online: https:\/\/www.google.com.hk\/url?sa=t&source=web&rct=j&opi=89978449&url=https:\/\/www.efgs.info\/wp-content\/uploads\/2020\/10\/EFGS_2020_session2_20.10.2020_Greg-Scott.pdf&ved=2ahUKEwjltf71uoqKAxU3avUHHfgYHWwQFnoECBsQAQ&usg=AOvVaw1FM6Wl28qjtGAA5t1RY33d."},{"key":"ref_37","unstructured":"Nations, U. (2015). Transforming Our World: The 2030 Agenda for Sustainable Development, United Nations, Department of Economic and Social Affairs."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s42256-019-0088-2","article-title":"The global landscape of AI ethics guidelines","volume":"1","author":"Jobin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104142","DOI":"10.1016\/j.cviu.2024.104142","article-title":"Embedding AI ethics into the design and use of computer vision technology for consumer\u2019s behaviour understanding","volume":"248","author":"Tiribelli","year":"2024","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s00146-022-01455-6","article-title":"Beyond bias and discrimination: Redefining the AI ethics principle of fairness in healthcare machine-learning algorithms","volume":"38","author":"Giovanola","year":"2023","journal-title":"AI Soc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/MC.2023.3235987","article-title":"Accountable deep-learning-based vision systems for preterm infant monitoring","volume":"56","author":"Migliorelli","year":"2023","journal-title":"Computer"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3457607","article-title":"A survey on bias and fairness in machine learning","volume":"54","author":"Mehrabi","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Suresh, H., and Guttag, J. (2021, January 5\u20139). A framework for understanding sources of harm throughout the machine learning life cycle. Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, Virtually.","DOI":"10.1145\/3465416.3483305"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Olteanu, A., Castillo, C., Diaz, F., and K\u0131c\u0131man, E. (2019). Social data: Biases, methodological pitfalls, and ethical boundaries. Front. Big Data, 2.","DOI":"10.3389\/fdata.2019.00013"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"118888","DOI":"10.1016\/j.eswa.2022.118888","article-title":"Quod erat demonstrandum?\u2014Towards a typology of the concept of explanation for the design of explainable AI","volume":"213","author":"Cabitza","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.ins.2022.10.013","article-title":"Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey","volume":"615","author":"Ding","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pesaresi, S., Mancini, A., Quattrini, G., and Casavecchia, S. (2022). Functional analysis for habitat mapping in a special area of conservation using sentinel-2 time-series data. Remote Sens., 14.","DOI":"10.3390\/rs14051179"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4529\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:45:45Z","timestamp":1760114745000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/23\/4529"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"references-count":47,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16234529"],"URL":"https:\/\/doi.org\/10.3390\/rs16234529","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]}}}