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Monitoring these events at high spatial detail is essential for forest management and climate adaptation, yet many remain undetected when using medium-resolution satellite imagery, and manual reporting by authorities is time-consuming and inconsistent. Here we present a high-resolution, deep learning-ready dataset designed for the classification of forest disturbance types. It consists of ~17,500 image patches (500\u2009\u00d7\u2009500 pixels at 0.2\u2009m resolution) derived from digital orthophotos of Rhineland-Palatinate, Germany. Each patch includes five channels (red, green, blue, near-infrared, and object height) and a segmentation mask with labeled disturbance classes such as bark beetle damage, clear-cuts, and windthrow. To demonstrate its utility, we apply a deep learning model and assess the contribution of individual channels through ablation analysis. The model achieved an overall accuracy of 88.2%, with near-infrared and object height identified as the most informative channels. The dataset offers a high-resolution resource for advancing deep learning-based forest disturbance monitoring.<\/jats:p>","DOI":"10.1038\/s41597-026-07084-8","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:29:15Z","timestamp":1774513755000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An AI-ready remote sensing dataset for high-resolution forest disturbance mapping"],"prefix":"10.1038","volume":"13","author":[{"given":"Enmanuel","family":"Rodr\u00edguez-Paulino","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Stoffels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Schlerf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Achim","family":"R\u00f6der","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Udelhoven","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"7084_CR1","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1146\/annurev-arplant-102820-012804","volume":"73","author":"H Hartmann","year":"2022","unstructured":"Hartmann, H. et al. Climate change risks to global forest health: emergence of unexpected events of elevated tree mortality worldwide. Annual Review of Plant Biology 73, 673\u2013702, https:\/\/doi.org\/10.1146\/annurev-arplant-102820-012804 (2022).","journal-title":"Annual Review of Plant Biology"},{"key":"7084_CR2","doi-asserted-by":"publisher","first-page":"3570","DOI":"10.3390\/rs12213570","volume":"12","author":"S Holzwarth","year":"2020","unstructured":"Holzwarth, S. et al. Earth observation based monitoring of forests in Germany: a review. Remote Sensing 12, 3570, https:\/\/doi.org\/10.3390\/rs12213570 (2020).","journal-title":"Remote Sensing"},{"key":"7084_CR3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1038\/s41893-020-00609-y","volume":"4","author":"C Senf","year":"2021","unstructured":"Senf, C. & Seidl, R. Mapping the forest disturbance regimes of Europe. 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