Update README.md

README.md

@@ -17,20 +17,21 @@ This is a toy dataset for show casing model training and fine-tuning in tree cro
 
 We have taken a single Multispectral (RGBi) 2x2 km DOP20 image tile for Halle, Sachsen-Anhalt, from LVermGeo ST for the year of 2022. 
 
-We then sliced the tiles into subtiles of 100x100m `.tif` files, resulting in 400 subtiles.
+We then sliced the tiles into subtiles of 100x100m, resulting in 400 subtiles. These are provided as 4-band raster `.tif` files.
 
 We are manually labelling tree crowns in these subtiles based on an entropy-based active learning approach. The label classes have been provided below. 
 
-The labeling is done in QGIS and the results are provided as `.shp` files for each subtile.
-
-Halle DOP20: © [GeoBasis-DE / LVermGeo ST 2022](https://www.lvermgeo.sachsen-anhalt.de/de/gdp-digitale-orthophotos.html)
+The labeling is done in QGIS and the polygon vectors are provided as  ESRI-shape `.shp` files per subtile.
 
 ### Label Classes
 
-0 = tree
-1 = cluster of trees
-2 = unsure
-3 = dead trees (haven’t added yet)
+**0** = tree
+
+**1** = cluster of trees
+
+**2** = unsure
+
+**3** = dead trees (haven’t added yet)
 
 
 ### Dataset Description
@@ -39,3 +40,7 @@ Halle DOP20: © [GeoBasis-DE / LVermGeo ST 2022](https://www.lvermgeo.sachsen-an
 - **Funded by:** Helmholtz Center for Environmental Research - UFZ
 - **Contact:** [taimur.khan@ufz.de](mailto:taimur.khan@ufz.de)
 
+### References
+
+- Halle DOP20: © [GeoBasis-DE / LVermGeo ST 2022](https://www.lvermgeo.sachsen-anhalt.de/de/gdp-digitale-orthophotos.html)
+