Update README.md

README.md

@@ -11,7 +11,9 @@ size_categories:
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-The DRIFT (Domain-Adaptive Regression for Forest Monitoring) dataset:
+
+
+# The DRIFT (Domain-Adaptive Regression for Forest Monitoring) dataset
 
 Dataset download link: https://sid.erda.dk/share_redirect/f1Hmpeh6O2
 
@@ -23,8 +25,10 @@ Publication: ECCV 2024 proceeding: Get Your Embedding Space in Order: Domain-Ada
 
 ------------------------
 
+## Description
 
-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:
+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:
 
 1. Canopy height: average height value for pixels containing woody vegetation.
 
@@ -33,32 +37,28 @@ The DRIFT dataset includes 25k image patches collected in five European countrie
 3. 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).
+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).
+
+
+## Applications
 
 The dataset is a good choice for:
 
-image-level regression
+* image-level regression
 
-domain adaption for regression
+* domain adaption for regression
 
-remote sensing for forest applications
+* remote sensing for forest applications
 
 
 
-Citation:
+## Citation:
 
+```
 @InProceedings{10.1007/978-3-031-72980-5_6,
-author="Li, Sizhuo
-and Gominski, Dimitri
-and Brandt, Martin
-and Tong, Xiaoye
-and Ciais, Philippe",
-editor="Leonardis, Ale{\v{s}}
-and Ricci, Elisa
-and Roth, Stefan
-and Russakovsky, Olga
-and Sattler, Torsten
-and Varol, G{\"u}l",
+author="Li, Sizhuo and Gominski, Dimitri and Brandt, Martin and Tong, Xiaoye and Ciais, Philippe",
+editor="Leonardis, Ale{\v{s}} and Ricci, Elisa and Roth, Stefan and Russakovsky, Olga and Sattler, Torsten and Varol, G{\"u}l",
 title="Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring",
 booktitle="Computer Vision -- ECCV 2024",
 year="2024",