| --- |
| license: cc-by-4.0 |
| task_categories: |
| - feature-extraction |
| pretty_name: IndLands |
| size_categories: |
| - 1M<n<10M |
| tags: |
| - geospatial |
| - remote-sensing |
| - spatial-analysis |
| - benchmarking |
| --- |
| |
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| # IndLands : A Spatiotemporal dataset for region-aware landslide analysis from Multi-Source Remote Sensing Imagery |
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| 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 |
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| [IndLands Repository](https://anonymous.4open.science/r/Dataset) |
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| # Structure of the ML Ready Dataset |
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| Each ZIP archive corresponds to a single state and contains multi-modal data derived from remote sensing sources, including: |
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| * 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 |
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| **Note:** The full dataset, including intermediate preprocessing outputs generated at each stage of the pipeline, is available upon request. |
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| --- |
