--- task_categories: - zero-shot-classification language: - en --- # 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) | **Metadata.paraquet 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 Germany; b) used the OPENCLIP_SIGCLIP_400M on the Quasara Platform for embedding generation c) no pre-training, no labelling happened in the preparation of this dataset ## Uses # MajorTOM-DE Dataset The **MajorTOM-DE dataset** provides embeddings derived from high-resolution satellite images of the Germany 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 Germany region. - **Related Dataset:** The MajorTOM-DE dataset is closely related to the original **S2L2A** dataset. ## Features The MajorTOM-DE 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-DE 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.paraquet** | 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).| **Embedding.dat** | Column | Explanation | |---------------|-----------------------------------------------------------------------------------------------------| | ID | ID of the image/image part for which the embedding was calculated. | | dat_row | Row in the .dat file that can be used to match the embeddings to the MajorTOM datasets via metadata.paraquet dataset. | | 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. | | split_configs | Quasara auto split configuration | | embeddings | Vectors calculated from the image/image segment. |