---
task_categories:
- zero-shot-classification
language:
- en
license: apache-2.0
---
# Dataset Card for Dataset Name

<!-- Provide a quick summary of the dataset. -->

We downloaded satellite images from Major-TOM, provided by the European Space Agency, filtered for Europe, and used our vectorisation engine 'Synapsis' to extract vector embeddings with one of the latest embedding model.

## Datasource Details
|               | Value                                   |
|---------------|-----------------------------------------|
| Datasource    | Major-TOM/Core-S2L2A                    |
| Region        | box(5.98865807458, 47.3024876979, 15.0169958839, 54.983104153) (Covers whole of Europe) |
| Date Range    | ('2020-01-01', '2025-01-01')            |
| Cloud Cover   | (0, 10)                                 |
| No Data       | (0.0, 0.0)                               |

Organisation: https://huggingface.co/Major-TOM

Base Dataset: https://huggingface.co/datasets/Major-TOM/Core-S2L2A


<!-- Provide a longer summary of what this dataset is. -->
**Metadata.parquet File**

This dataset shows the relationship between our embeddings/vectors and Major TOM images for fast linking to other Major TOM datasets.

**Embedding.dat**

This dataset entails the vector embeddings calculated by Quasara.

What we did on our side is:

a) download the Major-TOM dataset and filter it for images showing Europe;

b) vectorising the entire Major-TOM image data;

c) using the OPENCLIP_SIGLIP_400M and our scalable Vectorisation Engine 'Synapsis' for embedding extraction.

There was no pre-training, labelling or finetuning happening to prepare the vector embeddings of the Major TOM dataset.

## Uses

Potential use cases for the dataset we came up with is the data exploration of the data using text prompts, image prompts, unsupervised clustering of images, building a RAG or even building a chat bot on top of the vector embeddings.
What can you come up with to build?
<!-- Address questions around how the dataset is intended to be used. -->

# MajorTOM-Europe Dataset

The **MajorTOM-Europe dataset** provides embeddings derived from high-resolution satellite images of the Europe region, generated using the OpenCLIP SigLIP model. These embeddings, extracted from images covering a range of geographic coordinates across Europe, provide a powerful tool for various applications.

## Dataset Information

- **Coordinates Info:** The embeddings cover a range of geographic coordinates across the Europe region.
- **Related Dataset:** The MajorTOM-Europe dataset is closely related to the original **S2L2A** dataset.

## Features

The MajorTOM-Europe dataset leverages CLIP's ability to relate textual descriptions to visual data, enabling more intuitive searches and analysis. This allows users to search among images using text-based queries effectively.

## Applications

The MajorTOM-Europe dataset can be utilized for various applications, including:

- **Monitoring Changes in Land Use and Land Cover:**
  - Track deforestation
  - Observe urban expansion
  - Monitor water body dynamics
  - Finding countless objects from airports, golf courses to wind farms


- **Precision Agriculture:**
  - Analyze crop health
  - Predict yields
  - Plan harvests

- **Climate Research:**
  - Study climate patterns
  - Monitor changes and impacts on regional and local levels


<!--direct use have to think still with de code snippet -->



## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

**Metadata.parquet**
| Column         | Explanation                                                                                   |
|----------------|-----------------------------------------------------------------------------------------------|
| grid_cell      | Coordinates in the Major TOM grid, enabling fast linking to other Major TOM datasets.         |
| grid_row_u     | Row identifier in the Major TOM grid for linking purposes.                                     |
| grid_row_r     | Another row identifier in the Major TOM grid for linking purposes.                              |
| centre_lat     | Latitude of the center of the image portion for which embedding has been computed.             |
| centre_lon     | Longitude of the center of the image portion for which embedding has been computed.            |
| timestamp      | Date and time of the original product in the %Y%m%dT%H%M%S format.                             |
| dat_row        | Row number in the .dat file associated with the data entry.                                    |
| unique_id      | Unique identifier combining grid_cell, timestamp, and possibly other parameters (e.g., parquet).|
| image_type    | Each image is split into 70 segments and vectorized. |
| coordinates   | Coordinates in the image that define the segment that was vectorized. Full images have no coordinates. |
| embedding_file    | Corresponding file that stores the embedding vector. |

**Embedding.dat**

| Column        | Explanation                                                                                         |
|---------------|-----------------------------------------------------------------------------------------------------|
| embeddings    | Vectors calculated from the image/image segment.                                                     |

The metadata.parquet file can be linked to the embedding.dat file using the columns embedding_file and dat_row. For a detailed example, refer to the read_dataset.py script.