{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T05:14:07Z","timestamp":1762838047756,"version":"build-2065373602"},"reference-count":89,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"IMT Bucharest Core Program PN 2307","award":["PN23070104"],"award-info":[{"award-number":["PN23070104"]}]},{"name":"EU4HEALTH","award":["101101187"],"award-info":[{"award-number":["101101187"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Both remote sensing and medical fields benefited a lot from the machine learning methods, originally developed for computer vision and multimedia. We investigate the applicability of the same data mining-based machine learning (ML) techniques for exploring the structure of both Earth observation (EO) and medical image data. Support Vector Machine (SVM) is an explainable active learning tool to discover the semantic relations between the EO image content classes, extending this technique further to medical images of various types. The EO image dataset was acquired by multispectral and radar sensors (WorldView-2, Sentinel-2, TerraSAR-X, Sentinel-1, RADARSAT-2, and Gaofen-3) from four different urban areas. In addition, medical images were acquired by camera, microscope, and computed tomography (CT). The methodology has been tested by several experts, and the semantic classification results were checked by either comparing them with reference data or through the feedback given by these experts in the field. The accuracy of the results amounts to 95% for the satellite images and 85% for the medical images. This study opens the pathway to correlate the information extracted from the EO images (e.g., quality-of-life-related environmental data) with that extracted from medical images (e.g., medical imaging disease phenotypes) to obtain geographically refined results in epidemiology.<\/jats:p>","DOI":"10.3390\/make7040140","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T17:51:43Z","timestamp":1762451503000},"page":"140","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Explainable Machine Learning from Remote Sensing to Medical Images\u2014Merging Medical and Environmental Data into Public Health Knowledge Maps"],"prefix":"10.3390","volume":"7","author":[{"given":"Liviu","family":"Bilteanu","sequence":"first","affiliation":[{"name":"Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, 050095 Bucharest, Romania"},{"name":"Department of Oncology, Carol Davila University of Medicine and Pharmacy, 252 Siseaua Fundeni, 022328 Bucharest, Romania"},{"name":"National Institute for Research and Development in Microtechnologies\u2014IMT Bucharest, 126A Erou Iancu Nicolae Street, 077190 Voluntari, Romania"}]},{"given":"Corneliu Octavian","family":"Dumitru","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute, German Aerospace Center, M\u00fcnchener Str. 20, 82234 Wessling, Germany"}]},{"given":"Andreea","family":"Dumachi","sequence":"additional","affiliation":[{"name":"National Institute for Research and Development in Microtechnologies\u2014IMT Bucharest, 126A Erou Iancu Nicolae Street, 077190 Voluntari, Romania"},{"name":"Department of Automatic Control and Systems Engineering, National University of Science and Technology Politehnica Bucharest, 313 Splaiul Independen\u021bei, 060042 Bucharest, Romania"}]},{"given":"Florin","family":"Alexandrescu","sequence":"additional","affiliation":[{"name":"National Institute for Research and Development in Microtechnologies\u2014IMT Bucharest, 126A Erou Iancu Nicolae Street, 077190 Voluntari, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9075-2286","authenticated-orcid":false,"given":"Radu","family":"Popa","sequence":"additional","affiliation":[{"name":"National Institute for Research and Development in Microtechnologies\u2014IMT Bucharest, 126A Erou Iancu Nicolae Street, 077190 Voluntari, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5713-4304","authenticated-orcid":false,"given":"Octavian","family":"Buiu","sequence":"additional","affiliation":[{"name":"National Institute for Research and Development in Microtechnologies\u2014IMT Bucharest, 126A Erou Iancu Nicolae Street, 077190 Voluntari, Romania"}]},{"given":"Andreea Iren","family":"Serban","sequence":"additional","affiliation":[{"name":"Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, 050095 Bucharest, Romania"},{"name":"Department of Preclinical Sciences, Faculty of Veterinary Medicine, University of Agronomic Sciences and Veterinary Medicine, 105 Splaiul Independen\u021bei, 050095 Bucharest, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1080\/17538947.2019.1585976","article-title":"Big Earth Data: Disruptive Changes in Earth Observation Data Management and Analysis?","volume":"13","author":"Sudmanns","year":"2020","journal-title":"Int. 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