Sentinel2Cap

Repository for managing, analyzing, and using the Sentinel2Cap dataset, which contains captions for remote sensing images, both manually annotated and automatically generated with Qwen3-VL-8B-Instruct.

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📁 Project Structure

├── scripts/ # Training, inference, and utility scripts

├── script_dataset/ # Dataset analysis and statistics scripts

├── Sentinel2Cap.zip # 12k manually annotated captions

├── Sentinel2Cap.parquet # Structured dataset metadata

├── Qwen3-VL-8B-Instruct_... # Outputs from two studies (different prompts)

├── install_flash_attn.sh # Flash Attention installation script

├── pyproject.toml # Project dependencies

├── .python-version

└── .gitignore

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📊 Dataset

Sentinel2Cap.zip

Contains 12,000 manually annotated captions associated with Sentinel-2 RGB, Sentinel-2 multi-spectral and Sentinel-1 SAR images with a pseudo-RGB representations.

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Sentinel2Cap.parquet

File containing structured metadata for each dataset sample.

Columns

• key
• image_index
• number_of_classes
• number_of_classes_30
• file_name
• path_to_S2 → path to Sentinel-2 image
• path_to_SM → path to reference maps
• set → train / val / test
• used
• month
• occurrences
• s1_name → associated Sentinel-1 image name

Example

key: N9999_R037_T29SNB_16_20 image_index: 431416 number_of_classes: 12 number_of_classes_30: 11 file_name: S2B_MSIL2A_20180326T112109_... path_to_S2: BigEarthNet-S2/S2B_MSIL2A_20180326T112109_... path_to_SM: Reference_Maps/S2B_MSIL2A_20180326T112109_... set: train used: True month: march occurrences: 3 s1_name: S1A_IW_GRDH_1SDV_20180327T064326_29SNB_16_20

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🤖 Model Outputs

Qwen3-VL-8B-Instruct

This file contains outputs from two studies performed using the same model:

• Model: Qwen3-VL-8B-Instruct
• Main difference: prompting strategies used for caption generation

Use cases:

• comparison of prompting strategies
• qualitative and quantitative analysis of generated captions

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⚙️ Setup

Install dependencies:

pip install -e .

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🚀 Usage

Dataset Preparation

Make sure that paths in the .parquet file are correctly set:

path_to_S2 → Sentinel-2 images path_to_SM → reference maps Training / Inference

Main scripts are located in:

scripts/

Examples:

python scripts/train.py python scripts/inference.py Dataset Analysis

Scripts available in:

script_dataset/

Useful for:

class distribution analysis temporal distribution caption analysis

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📌 Notes

The dataset combines information from:

Sentinel-2 (RGB imagery) Sentinel-2 (multi-spectral imagery) Sentinel-1 (SAR imagery) reference land cover maps

Manually annotated captions can be used as:

ground truth benchmark for generative models

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📄 License

Copyright (c) 2026 Tosato Lucrezia MIT License for Sentinel2Cap dataset

Copyright (c) 2026 Tosato Lucrezia, Gianluca Lombardi CC BY 4.0 License for the code

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✉️ Contact

Lucrezia Tosato: ltosato (at) sarmap.ch Gianluca Lombardi: gianluca.lombardi.fr (at) gmail.com Ronny Hansch: rww.haensch (at) gmail.com

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✅ Citation

The paper is under review; for the moment, please use the following citation: xxx