---
license: cc-by-4.0
task_categories:
- feature-extraction
pretty_name: IndLands
size_categories:
- 1M<n<10M
tags:
- geospatial
- remote-sensing
- spatial-analysis
- benchmarking
---




# IndLands : A Spatiotemporal dataset for region-aware landslide analysis from Multi-Source Remote Sensing Imagery

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:

<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 archive corresponds to a single state and contains multi-modal data derived from remote sensing sources, including:

* Raw Sentinel-2 image tiles
* Digital Elevation Model (DEM) data
* GLCM-based texture features
* Spectral index feature maps
* Spatial subsets
* Manually annotated landslide regions
* `dataset.csv`, providing a consolidated tabular view of all extracted features, annotations, and associated latitude–longitude coordinates

**Note:** The full dataset, including intermediate preprocessing outputs generated at each stage of the pipeline, is available upon request.

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