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README.md

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 pretty_name: Domain-Adaptive Regression for Forest Monitoring
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+Download link: https://sid.erda.dk/share_redirect/f1Hmpeh6O2
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+Introduced by Li et al. in ECCV 2024 proceeding: Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring
+The DRIFT dataset includes 25k image patches collected in five European countries sourced from aerial and nanosatellite image archives. Each image patch is associated with three target variables to predict:
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+Canopy height: average height value for pixels containing woody vegetation.
+Tree count: number of overstory (visible from an overhead perspective) trees in the images.
+Tree cover fraction: percentage of the image being covered by overstory tree crowns.
+The DRIFT dataset includes significant shifts between label and visual distributions due to sensor and area differences. Furthermore, vegetation tends to grow to fit the local climate, therefore introducing concept drift in the data: same tree species may appear differently in different subsets. The label distribution also varies among different subsets (countries).
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+The dataset is a good choice for:
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+image-level regression
+domain adaption for regression
+remote sensing for forest applications