update read me, img embedding not working

README.md

@@ -9,8 +9,33 @@ size_categories:
 - 100K<n<1M
 ---
 
-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
+# BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series [https://nascetti-a.github.io/BioMasster/]
+
+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
+
+### 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 src="https:"https://huggingface.co/datasets/nascetti-a/BioMassters/blob/main/Data_specifications.png">
+
+### Data Size:
+
+```
+  dataset         | # files  | size
+  --------------------------------------
+  train_features  |  189078  | 215.9GB
+  test_features   |   63348  |  73.0GB
+  train_agbm      |    8689  |   2.1GB
+```
+### Contact Information:
+
 
-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.