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--- |
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task_categories: |
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- zero-shot-classification |
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language: |
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- en |
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license: apache-2.0 |
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--- |
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# Dataset Card for Dataset Name |
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<!-- Provide a quick summary of the dataset. --> |
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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. |
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## Datasource Details |
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|---------------|-----------------------------------------| |
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| Datasource | Major-TOM/Core-S2L2A | |
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| Region | box(5.98865807458, 47.3024876979, 15.0169958839, 54.983104153) (Covers whole of Europe) | |
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| Date Range | ('2020-01-01', '2025-01-01') | |
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| Cloud Cover | (0, 10) | |
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| No Data | (0.0, 0.0) | |
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Organisation: https://huggingface.co/Major-TOM |
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Base Dataset: https://huggingface.co/datasets/Major-TOM/Core-S2L2A |
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<!-- Provide a longer summary of what this dataset is. --> |
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**Metadata.parquet File** |
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This dataset shows the relationship between our embeddings/vectors and Major TOM images for fast linking to other Major TOM datasets. |
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**Embedding.dat** |
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This dataset entails the vector embeddings calculated by Quasara. |
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What we did on our side is: |
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a) download the Major-TOM dataset and filter it for images showing Europe; |
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b) vectorising the entire Major-TOM image data; |
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c) using the OPENCLIP_SIGLIP_400M and our scalable Vectorisation Engine 'Synapsis' for embedding extraction. |
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There was no pre-training, labelling or finetuning happening to prepare the vector embeddings of the Major TOM dataset. |
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## Uses |
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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. |
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What can you come up with to build? |
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<!-- Address questions around how the dataset is intended to be used. --> |
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# MajorTOM-Europe Dataset |
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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. |
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## Dataset Information |
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- **Coordinates Info:** The embeddings cover a range of geographic coordinates across the Europe region. |
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- **Related Dataset:** The MajorTOM-Europe dataset is closely related to the original **S2L2A** dataset. |
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## Features |
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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. |
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## Applications |
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The MajorTOM-Europe dataset can be utilized for various applications, including: |
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- **Monitoring Changes in Land Use and Land Cover:** |
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- Track deforestation |
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- Observe urban expansion |
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- Monitor water body dynamics |
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- Finding countless objects from airports, golf courses to wind farms |
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- **Precision Agriculture:** |
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- Analyze crop health |
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- Predict yields |
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- Plan harvests |
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- **Climate Research:** |
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- Study climate patterns |
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- Monitor changes and impacts on regional and local levels |
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<!--direct use have to think still with de code snippet --> |
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## Dataset Structure |
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<!-- 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. --> |
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**Metadata.parquet** |
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| Column | Explanation | |
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|----------------|-----------------------------------------------------------------------------------------------| |
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| grid_cell | Coordinates in the Major TOM grid, enabling fast linking to other Major TOM datasets. | |
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| grid_row_u | Row identifier in the Major TOM grid for linking purposes. | |
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| grid_row_r | Another row identifier in the Major TOM grid for linking purposes. | |
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| centre_lat | Latitude of the center of the image portion for which embedding has been computed. | |
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| centre_lon | Longitude of the center of the image portion for which embedding has been computed. | |
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| timestamp | Date and time of the original product in the %Y%m%dT%H%M%S format. | |
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| dat_row | Row number in the .dat file associated with the data entry. | |
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| unique_id | Unique identifier combining grid_cell, timestamp, and possibly other parameters (e.g., parquet).| |
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| image_type | Each image is split into 70 segments and vectorized. | |
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| coordinates | Coordinates in the image that define the segment that was vectorized. Full images have no coordinates. | |
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| embedding_file | Corresponding file that stores the embedding vector. | |
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**Embedding.dat** |
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| Column | Explanation | |
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|---------------|-----------------------------------------------------------------------------------------------------| |
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| embeddings | Vectors calculated from the image/image segment. | |
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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. |
