metadata

task_categories:
  - zero-shot-classification
language:
  - en
license: apache-2.0

Dataset Card for Dataset Name

We downloaded satellite images from Major-TOM, filtered for Germany, and processed them into vector embeddings.

Datasource Details

	Value
Datasource	Major-TOM/Core-S2L2A
Region	box(5.98865807458, 47.3024876979, 15.0169958839, 54.983104153) (Covers whole of Germany)
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

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 has the vector embeddings calculated by us.

What we did was:

a) to vectorise the entire Major-TOM image data for Europe;

b) used the OPENCLIP_SIGLIP_400M on the Quasara Platform for embedding generation

c) no pre-training, no labelling happened in the preparation of this dataset

Uses

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 Germany, 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
Precision Agriculture:
- Analyze crop health
- Predict yields
- Plan harvests
Climate Research:
- Study climate patterns
- Monitor changes and impacts on regional and local levels

Dataset Structure

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.