Update README.md

README.md

@@ -10,10 +10,33 @@ size_categories:
 
 
 
-# IndLands Dataset
+# IndLands : A Spatiotemporal dataset for region-aware landslide analysis from Multi-Source Remote Sensing Imagery
 
-This dataset contains landslide data considering events from seven Indian states: Himachal Pradesh, Mizoram, Sikkim, and Uttarakhand as our study region
+This repository contains the complete workflow and supporting files for generating a **landslide-prone area dataset** using remote sensing and machine learning techniques. The dataset has been prepared for the following Indian states:
 
-## Structure of the ML Ready Dataset
+<table>
+  <tr>
+    <td>
+      <ul>
+        <li><strong>Uttarakhand</strong></li>
+       <li><strong>Sikkim</strong></li>
+        <li><strong>Himachal Pradesh</strong></li>
+       <li><strong>Mizoram</strong></li>
+        <li><strong>Maharashtra</strong></li>
+        <li><strong>Karnataka</strong></li>
+        <li><strong>Arunachal Pradesh</strong></li>
+      </ul>
+    </td>
+    <td>
+      <img src="https://cdn-uploads.huggingface.co/production/uploads/6824219fec148952b8c153d2/xoQ5tt55-kRYlGA6ndzm1.png" width="300" height="400" alt="Indian map with marked states" />
+    </td>
+   </tr>
+</table>
+
+The final output is a multi-modal dataset containing terrain, spectral, and texture-based features extracted from satellite data, along with manually annotated landslide zones for training machine learning models.The complete workflow and steps can be accessible from here
+
+[IndLands Repository](https://anonymous.4open.science/r/Dataset)
+
+# Structure of the ML Ready Dataset
 
 Each zip folder represents each state under which raw tiles (sentinel2), DEM, GLCM, Indices feature images, Subsets, Annotation and dataset.csv (tabular representation of all the features,annotations and latitude,longitude)