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---
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. |