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README.md
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## Characteristics and Challenges
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- Long-Tail Distribution: The dataset exhibits a long-tail distribution common in natural world settings, making it a realistic benchmark for machine learning applications.
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- Geographic Bias: The dataset reflects the geographic bias of citizen science data, with more observations from densely populated and visited regions like urban areas and National Parks.
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- Many-to-One Pairing: There are instances where multiple ground-level images are paired to the same aerial image.
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## Splits
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- Training Set:
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## Characteristics and Challenges
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- Long-Tail Distribution: The dataset exhibits a long-tail distribution common in natural world settings, making it a realistic benchmark for machine learning applications.
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- Geographic Bias: The dataset reflects the geographic bias of citizen science data, with more observations from densely populated and visited regions like urban areas and National Parks.
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- Many-to-One Pairing: There are instances in the dataset where multiple ground-level images are paired to the same aerial image.
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## Splits
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- Training Set:
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