| # Exploratory Data Analysis: Rock Glacier Dataset |
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| ## 1. Basic Dataset Information |
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| | Property | Value | Description | |
| | :--- | :--- | :--- | |
| | **Total Features** | `626` | The total number of rock glacier polygons in the dataset. | |
| | **Geometry Type** | `Polygon` | The geometric shape format of the data. | |
| | **CRS** | `EPSG:4326` | Coordinate Reference System (Standard Latitude/Longitude, WGS 84). | |
| | **Bounding Box** | `[88.0747, 27.3212]` to `[88.9281, 28.0849]` | The geographic extent (Longitude / Latitude). This falls roughly in the Sikkim Himalayas region. | |
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| ## 2. Attribute Table Analysis |
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| The shapefile contains **20 columns**, many of which are empty or artifacts from a previous format conversion (likely KML/KMZ to Shapefile). |
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| ### Useful Columns |
| These columns contain data that may be relevant for identifying or tracking the source of the polygons: |
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| | Column Name | Data Type | Description & Preview | |
| | :--- | :--- | :--- | |
| | `Name` | String | IDs or names (e.g., `"1"`, `"2"`, `"NULL01"`). | |
| | `id` | String | A secondary ID field present in some rows. | |
| | `layer` | String | Source layer name (e.g., `finl_sikm_shp`). | |
| | `path` | String | The original file path of the source data. | |
| | `geometry` | Geometry | The actual polygon boundary coordinates. **(Crucial for deep learning masks)** | |
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| ### Unused / Empty Columns |
| The following columns are mostly filled with `None`, `NaN`, or default values (`1`, `0`, `-1`) and can be safely ignored or dropped during the data preprocessing phase: |
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| - **Empty Data**: `descriptio`, `timestamp`, `begin`, `end`, `altitudeMo`, `shp_05`, `descript_1`, `altitude_1`, `RG_AJ`, `lohanak` |
| - **KML Artifacts**: `tessellate`, `extrude`, `visibility`, `drawOrder`, `icon` |
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| ## 3. Data Preview (First 5 Rows) |
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| Below is a cleaned snippet of the first 5 entries showing only the most relevant columns, instead of the messy raw pandas output: |
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| | Index (Hidden ID) | Name | layer | path | id | geometry | |
| | :--- | :--- | :--- | :--- | :--- | :--- | |
| | 0 | 1 | *NaN* | *NaN* | *NaN* | POLYGON Z ((88.0865... | |
| | 1 | 2 | *NaN* | *NaN* | *NaN* | POLYGON Z ((88.0864... | |
| | 2 | 3 | *NaN* | *NaN* | *NaN* | POLYGON Z ((88.0863... | |
| | 3 | 3 | *NaN* | *NaN* | *NaN* | POLYGON Z ((88.0863... | |
| | 4 | 4 | *NaN* | *NaN* | *NaN* | POLYGON Z ((88.0862... | |
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| ## 4. Recommendations for Deep Learning Pipeline |
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| 1. **Geometry Rasterization**: For your deep learning models (U-Net, SegFormer), you only need the `geometry` column. These polygons will be rasterized to create binary masks where `1 = Rock Glacier` and `0 = Background`. |
| 2. **Coordinate Projection**: Since the dataset is in degrees (`EPSG:4326`), ensure that your input rasters (Sentinel-2, DEM) match this CRS. If you need to extract patches of a specific size in meters (e.g., 256x256 pixels at 10m/pixel), you should project this shapefile to a local UTM zone (in meters) before patching. |
| 3. **Data Cleaning**: You can completely drop the 15+ unused KML columns when loading this shapefile with `geopandas` to save memory and keep the pipeline clean. |
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