Update README.md

README.md

@@ -59,24 +59,7 @@ TreeFinder supports the following machine learning tasks:
 
 ## Dataset Structure
 
-Each sample contains a high-resolution NAIP image patch, a corresponding binary segmentation mask, and associated metadata.
-
-A typical sample contains:
-
-```python
-{
-  "image": "path/to/image_patch.tif",
-  "mask": "path/to/mask_patch.tif",
-  "site_id": "...",
-  "patch_id": "...",
-  "latitude": ...,
-  "longitude": ...,
-  "state": "...",
-  "climate_zone": "...",
-  "tree_type": "...",
-  "split": "train/validation/test"
-}
-```
+Each sample contains a high-resolution NAIP image patch and a corresponding binary segmentation mask.
 
 The imagery contains four channels:
 
@@ -91,6 +74,9 @@ The segmentation mask is binary:
 1 = dead tree
 ```
 
+The associated metadata is available in the .csv file. 
+
+
 ## Data Sources
 
 TreeFinder is derived from high-resolution NAIP aerial imagery covering forested regions across CONUS. Dead tree annotations were manually created and validated using expert interpretation and multi-temporal image comparison.