Update README.md

README.md

@@ -19,4 +19,44 @@ The **Sentinel-2 Land-cover Captioning Dataset** (**S2LCD**) is a newly proposed
 
 Each image patch is accompanied by five captions exported in COCO format, resulting in a total of **7665** captions. These captions employ a broad vocabulary that combines natural language and the EAGLES lexicon, ensuring meticulous attention to detail.
 
-This dataset was introduced in our **paper/repository**: [RSDiX: Lightweight and Data-Efficient VLMs for Remote Sensing through Self-Distillation](https://github.com/NeuRoNeLab/RSDiX-CLIP?tab=readme-ov-file#models-weights). Please refer to it for further details on data collection, captioning methodology, and use cases.
+The creation of this dataset involved three key activities:
+
+1. **Definition of the Reference Taxonomy:** Establishing a language for describing scenes extracted from satellite images. This lexicon aims to balance the use of natural language for describing image content, ensuring caption variety and the incorporation of specific land use and land cover domain terms. The reference taxonomy is defined and proposed by the EAGLE group (EIONET Action Group on Land Monitoring in Europe), which serves as a standard lexicon in Europe for soil monitoring analyses based on satellite data.
+
+2. **Extraction Procedure for Individual Images:** Sentinel-2 satellite images are distributed in multi-layer JP2 format, with one file for each spectral band and usually huge dimensions. Thus, a critical step in the dataset construction is to extract individual **224 × 224** RGB image patches from raw Sentinel-2 data. A custom Python tool was developed to this end.
+
+3. **Caption Creation:** Annotation of each extracted **224 × 224** RGB image patch, ensuring linguistic variety and usage of domain-specific terms with the EAGLE lexicon was accomplished.
+
+This dataset was introduced in our **paper/repository**: [RSDiX: Lightweight and Data-Efficient VLMs for Remote Sensing through Self-Distillation](https://github.com/NeuRoNeLab/RSDiX-CLIP). Please refer to it for further details on data collection, captioning methodology, and use cases.
+
+## Dataset Structure
+The `dataset_s2lcd.json` file contains caption annotations for Sentinel-2 image patches. Each entry in the `images` array corresponds to a single image patch and includes:
+
+- **`filename`**: The name of the image file (e.g., `"S2A_L1C_20150925_N0204R065_2329.tif"`).
+- **`imgid`**: A unique integer identifier for the image.
+- **`sentences`**: A list of five captions describing the image. Each caption is stored in a dictionary under the key `raw`, containing a natural language description of land use and land cover features visible in the image.
+
+Example structure:
+```json
+{
+  "images": [
+    {
+      "filename": "example_image.tif",
+      "imgid": 0,
+      "sentences": [
+        { "raw": "Caption 1" },
+        { "raw": "Caption 2" },
+        ...
+      ]
+    },
+    ...
+  ]
+}
+```
+
+## Other Sources
+
+You can also access this dataset on [Kaggle](https://www.kaggle.com/datasets/angelonazzaro/sentinel-2-land-cover-captioning-dataset) and on our [Github repository](https://github.com/NeuRoNeLab/RSDiX-CLIP).
+
+## License
+This dataset is released under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.