An AI-ready remote sensing dataset for high-resolution forest disturbance mapping.
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| Title: | An AI-ready remote sensing dataset for high-resolution forest disturbance mapping. |
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| Authors: | Rodríguez-Paulino E; Remote Sensing and Natural Resources Modelling Group, Luxembourg Institute of Science and Technology (LIST), Belvaux, 41, rue du Brill, Luxembourg, L-4422, Germany. enmanuel.rodpau@gmail.com.; Earth Observation and Climate Processes, Trier University, Behringstr. 21, Trier, 54286, Germany. enmanuel.rodpau@gmail.com., Stoffels J; Earth Observation and Climate Processes, Trier University, Behringstr. 21, Trier, 54286, Germany., Schlerf M; Remote Sensing and Natural Resources Modelling Group, Luxembourg Institute of Science and Technology (LIST), Belvaux, 41, rue du Brill, Luxembourg, L-4422, Germany., Röder A; Earth Observation and Climate Processes, Trier University, Behringstr. 21, Trier, 54286, Germany., Wagner A; Forschungsanstalt für Waldökologie und Forstwirtschaft, Landesforsten Rheinland-Pfalz, Hauptstraße 16, Trippstadt, 67705, Germany., Udelhoven T; Earth Observation and Climate Processes, Trier University, Behringstr. 21, Trier, 54286, Germany. |
| Source: | Scientific data [Sci Data] 2026 Mar 26; Vol. 13 (1). Date of Electronic Publication: 2026 Mar 26. |
| Publication Type: | Journal Article; Dataset |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101640192 Publication Model: Electronic Cited Medium: Internet ISSN: 2052-4463 (Electronic) Linking ISSN: 20524463 NLM ISO Abbreviation: Sci Data Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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