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andyvhuynh commited on Jul 17

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@@ -67,7 +67,7 @@ To encourage development of better machine learning methods for operating with d
 ## Characteristics and Challenges
 - Long-Tail Distribution: The dataset exhibits a long-tail distribution common in natural world settings, making it a realistic benchmark for machine learning applications.
 - 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.
-- Many-to-One Pairing: There are instances where multiple ground-level images are paired to the same aerial image.
 ## Splits
 - Training Set:

 ## Characteristics and Challenges
 - Long-Tail Distribution: The dataset exhibits a long-tail distribution common in natural world settings, making it a realistic benchmark for machine learning applications.
 - 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.
+- Many-to-One Pairing: There are instances in the dataset where multiple ground-level images are paired to the same aerial image.
 ## Splits
 - Training Set: