{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T17:20:47Z","timestamp":1778174447781,"version":"3.51.4"},"reference-count":77,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/04152\/2020"],"award-info":[{"award-number":["UIDB\/04152\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["PRT\/BD\/153517\/2021"],"award-info":[{"award-number":["PRT\/BD\/153517\/2021"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/04152\/2020"],"award-info":[{"award-number":["UIDB\/04152\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["PRT\/BD\/153517\/2021"],"award-info":[{"award-number":["PRT\/BD\/153517\/2021"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convolutional neural networks rely on fully pixel-level annotated images, which are difficult to obtain. Although reference LC datasets have been widely used to derive annotations, NFIs consist of point-based data, providing only sparse annotations. Weakly supervised and self-supervised learning approaches help address this issue by reducing dependence on fully annotated images and leveraging unlabeled data. However, their potential for large-scale LC mapping needs further investigation. This study explored the use of NFI data with deep learning and weakly supervised and self-supervised methods. Using Sentinel-2 images and the Portuguese NFI, which covers other LC types beyond forest, as sparse labels, we performed weakly supervised semantic segmentation with a convolutional neural network to create an updated and spatially continuous national LC map. Additionally, we investigated the potential of self-supervised learning by pretraining a masked autoencoder on 65,000 Sentinel-2 image chips and then fine-tuning the model with NFI-derived sparse labels. The weakly supervised baseline achieved a validation accuracy of 69.60%, surpassing Random Forest (67.90%). The self-supervised model achieved 71.29%, performing on par with the baseline using half the training data. The results demonstrated that integrating both learning approaches enabled successful countrywide LC mapping with limited training data.<\/jats:p>","DOI":"10.3390\/rs17040711","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T05:34:26Z","timestamp":1739943266000},"page":"711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Weakly Supervised and Self-Supervised Learning Approach for Semantic Segmentation of Land Cover in Satellite Images with National Forest Inventory Data"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4568-8182","authenticated-orcid":false,"given":"Daniel","family":"Moraes","sequence":"first","affiliation":[{"name":"Nova Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal"},{"name":"Dire\u00e7\u00e3o-Geral do Territ\u00f3rio, Rua da Artilharia Um 107, 1099-052 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9634-3061","authenticated-orcid":false,"given":"Manuel L.","family":"Campagnolo","sequence":"additional","affiliation":[{"name":"Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8913-7342","authenticated-orcid":false,"given":"M\u00e1rio","family":"Caetano","sequence":"additional","affiliation":[{"name":"Nova Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal"},{"name":"Dire\u00e7\u00e3o-Geral do Territ\u00f3rio, Rua da Artilharia Um 107, 1099-052 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1126\/science.aac8083","article-title":"Climate Change: Biophysical Climate Impacts of Recent Changes in Global Forest Cover","volume":"351","author":"Alkama","year":"2016","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1126\/science.1118160","article-title":"Atmospheric Science: The Importance of Land-Cover Change in Simulating Future Climates","volume":"310","author":"Feddema","year":"2005","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/17474230601079316","article-title":"Evolving Standards in Land Cover Characterization","volume":"1","author":"Herold","year":"2006","journal-title":"J. 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