MapPool Bubbling up an extremely large corpus of maps for AI MapPool is a dataset of 75 million potential maps and textual captions. It has been derived from CommonPool, a dataset consisting of 12 billion text image pairs from the Internet. The images have been encoded by a vision transformer and classified into maps and non maps by a support vector machine. This approach outperforms previous models and yields a validation accuracy of 98.5%. The MapPool dataset may help to train data intensive architectures in order to establish vision and language foundation models specialized in maps. The analysis of the dataset and the exploration of the embedding space offers a large potential for future work. How is the data structured? Key Meaning uid Unique identifier url Link to the image text Textual description of the image original width / original height Dimensions of the image sha256 Hash of the image (to verify if the image is the same as the one in the URL) l14 img Embedding of the image (768 dimensions) l14 txt Embedding of the textual description (768 dimensions) clip l14 similarity score Similarity between the image and text (higher values indicate higher similarity) How can this…
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