updates (#3)

- updates (756f570f8af48a18705bc49a467ea898194bf335)


Co-authored-by: Ritu Yadav <Rituxx96x@users.noreply.huggingface.co>

README.md

@@ -8,9 +8,31 @@ pretty_name: BioMassters
 size_categories:
 - 100K<n<1M
 ---
+# BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series [https://nascetti-a.github.io/BioMasster/]
 
-The goal of this dataset is to test deep learning algorithms that predict yearly Above Ground Biomass (AGB) for Finnish forests using satellite imagery. 
-Feature data: Satellite imagery from the European Space Agency and European Commission's joint Sentinel-1 and Sentinel-2 satellite missions, designed to collect a rich array of Earth observation data
+The objective of this repository is to provide a deep learning ready dataset to predict yearly Above Ground Biomass (AGB) for Finnish forests using multi-temporal satellite imagery from
+the European Space Agency and European Commission's joint Sentinel-1 and Sentinel-2 satellite missions, designed to collect a rich array of Earth observation data
 
-Label data: Ground-truth AGB measurements collected using LiDAR (Light Detection and Ranging) calibrated with in-situ measurements. LiDAR is able to generate high-quality AGB maps, but is more time consuming and intensive to collect than satellite imagery.
+### Reference data: 
+* Reference AGB measurements were collected using LiDAR (Light Detection and Ranging) calibrated with in-situ measurements.
+* Total 13000 patches, each patch covering 2,560 by 2,560 meter area.
 
+### Feature data: 
+* Sentinel-1 SAR and Sentinel-2 MSI data
+* 12 months of data (1 image per month)
+* Total 310,000 patches
+
+### Data Specifications:
+![img](./Data_specifications.png)
+
+### Data Size:
+
+```
+  dataset         | # files  | size
+  --------------------------------------
+  train_features  |  189078  | 215.9GB
+  test_features   |   63348  |  73.0GB
+  train_agbm      |    8689  |   2.1GB
+```
+
+## Citation : under review