Added Dataset Download

README.md

@@ -67,11 +67,14 @@ The 3,854 chips have been randomly split into training (80%) and validation (20%
 
 ## Dataset Creation
 ### Query and Scene Selection
-First, a set of 5,000 chips was defined based on samples from the USDA CDL to ensure a representative sampling across the CONUS. Next, for each chip, the corresponding HLS S30 scenes between March and September 2022 were queried, and scenes with low cloud cover were retrieved. Then, three scenes are selected among the low cloudy scenes to ensure a scene from early in the season, one in the middle, and one toward the end. The three final scenes were then reprojected to CDL's projection grid (`EPSG:5070`) using bilinear interpolation. 
+First, a set of 5,000 chips were defined based on samples from the USDA CDL to ensure a representative sampling across the CONUS. Next, for each chip, the corresponding HLS S30 scenes between March and September 2022 were queried, and scenes with low cloud cover were retrieved. Then, three scenes are selected among the low cloudy scenes to ensure a scene from early in the season, one in the middle, and one toward the end. The three final scenes were then reprojected to CDL's projection grid (`EPSG:5070`) using bilinear interpolation. 
 
 ### Chip Generation
 In the final step, the three scenes for each chip were clipped to the bounding box of the chip, and 18 spectral bands were stacked together. In addition, a quality control was applied to each chip using the `Fmask` layer of the HLS dataset. Any chip containing clouds, cloud shadow, adjacent to cloud or missing values were discarded. This resulted in 3,854 chips.
 
+### Dataset Download
+You can download the data in `.tgz` format from this repository (you need to install [Git Large File Sotrage](https://git-lfs.com/) for this). The same version of the data is hosted on [Source Cooperative](https://beta.source.coop/repositories/clarkcga/multi-temporal-crop-classification/description) as objects on AWS S3. 
+
 ### Citation
 
 If this dataset helped your research, please cite `hls-multi-temporal-crop-classification` in your publications. Here is an example BibTeX entry: