Copy for LLM[View as Markdown](https://docs.planet.com/data/public-data/other-datasets/cnes-land-cover-map/)

# CNES Land Cover Map

![Header Thumbnail](/data/public-data/other-datasets/cnes-land-cover-map-lyon.webp)

The CNES Land Cover Map (Occupation des Sols, OSO) produces land classification for Metropolitan France at a 10m spatial resolution. It is based on Sentinel-2 L2A data processed within the Theia Land Cover CES framework. The map is generated automatically using the iota² processing chain, which employs a Random Forest classifier calibrated with extensive national vector data (such as BD TOPO and Corine Land Cover).

## Data Availability & Collections

The OSO maps are available as annual composites. Since 2018, the product has used a 23-category nomenclature, which is backward-compatible with the 17-category version used in 2016 and 2017.

* **Collection ID**: `9baa2732-6597-49d2-ae3b-68ba0a5386b2`

* **Update Frequency**: Annually.

## Basic Facts

| Property                | Value                                                                                                                |
| ----------------------- | -------------------------------------------------------------------------------------------------------------------- |
| **Sensor**              | MultiSpectral Instrument (MSI) from Sentinel-2                                                                       |
| **Spatial Resolution**  | 10 m                                                                                                                 |
| **Geographic Coverage** | Metropolitan France                                                                                                  |
| **Coordinate System**   | Lambert-93 (EPSG:2154) or UTM                                                                                        |
| **Data Format**         | Cloud Optimized GeoTIFF (COG)                                                                                        |
| **Temporal Coverage**   | 2016 – 2022 (Updated annually)                                                                                       |
| **License**             | [ETALAB V2.0 Open License](https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf) |
| **Provider**            | **Planet**                                                                                                           |

## Band Information

The CNES Land Cover product contains three specific bands:

| Name                | Description                                                                 |
| ------------------- | --------------------------------------------------------------------------- |
| **OCS**             | Main discrete classification according to the 23-category nomenclature.     |
| **OCS\_Confidence** | Classifier confidence level (values 1 to 100).                              |
| **OCS\_Validity**   | Indicates the number of cloudless images used for the pixel classification. |

## Class Definitions (23-Category Nomenclature)

The following table describes the classification values found in the OCS band for maps from 2018 onwards.

| Value  | Label                                    | Color     |
| ------ | ---------------------------------------- | --------- |
| **1**  | Dense built-up area                      | `#ff00ff` |
| **2**  | Diffuse built-up area                    | `#ff55ff` |
| **3**  | Industrial and commercial areas          | `#ffaaff` |
| **4**  | Roads                                    | `#00ffff` |
| **5**  | Oilseeds (Rapeseed)                      | `#ffff00` |
| **6**  | Straw cereals (Wheat, Triticale, Barley) | `#d0ff00` |
| **7**  | Protein crops (Beans / Peas)             | `#a1d600` |
| **8**  | Soy                                      | `#ffab44` |
| **9**  | Sunflower                                | `#d6d600` |
| **10** | Corn                                     | `#ff5500` |
| **11** | Rice                                     | `#c5ffff` |
| **12** | Tubers/roots                             | `#aaaa61` |
| **13** | Grasslands                               | `#aaaa00` |
| **14** | Orchards and fruit growing               | `#aaaaff` |
| **15** | Vineyards                                | `#550000` |
| **16** | Hardwood forest                          | `#009c00` |
| **17** | Softwood forest                          | `#003200` |
| **18** | Natural grasslands and pastures          | `#aaff00` |
| **19** | Woody moorlands                          | `#55aa7f` |
| **20** | Natural mineral surfaces                 | `#ff0000` |
| **21** | Beaches and dunes                        | `#ffb802` |
| **22** | Glaciers and eternal snows               | `#bebebe` |
| **23** | Water                                    | `#0000ff` |

## Accessing the Data

### Planet Insights Platform API

To access this collection programmatically, use the following details in your API requests:

* **Endpoint**: `services.sentinel-hub.com`

* **Collection Type**: `byoc-9baa2732-6597-49d2-ae3b-68ba0a5386b2`

### Visualization Script (Evalscript)

Use this script to visualize the land cover classification with the official CNES color scheme:

```
// VERSION=3

// CNES Land Cover (OSO) Visualizer

const colormap = [

  [1, 0xff00ff], [2, 0xff55ff], [3, 0ffaaff], [4, 0x00ffff],

  [5, 0xffff00], [6, 0xd0ff00], [7, 0xa1d600], [8, 0xffab44],

  [9, 0xd6d600], [10, 0xff5500], [11, 0xc5ffff], [12, 0 aaaa61],

  [13, 0 aaaa00], [14, 0 aaaaff], [15, 0x550000], [16, 0x009c00],

  [17, 0x003200], [18, 0 aaff00], [19, 0x55aa7f], [20, 0xff0000],

  [21, 0xffb802], [22, 0xbebebe], [23, 0x0000ff]

];



const visualizer = new ColorMapVisualizer(colormap);



function setup() {

  return {

    input: ["OCS", "dataMask"],

    output: { bands: 3 }

  };

}



function evaluatePixel(samples) {

  return visualizer.process(samples.OCS);

}
```

## Attribution

Value-added data processed by CNES for the [Theia](https://www.theia-land.fr/en/homepage-en/#) data center from Copernicus data. Processing uses algorithms developed by Theia's Centers of Scientific Expertise.