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Update README.md
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README.md
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sequence: int64
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# Dataset Overview
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- Total Images: Over 3 million ground-level and aerial image pairs
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- Species Covered: Over 6,000 native and introduced plant species
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- Geographic Focus: California, USA
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- Ground-Level Images:
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- Sourced from iNaturalist open data on AWS.
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- Filters applied:
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- RGB-Infrared images, 256x256 pixels, 60 cm-per-pixel resolution.
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- Centered on the latitude and longitude of the iNaturalist observation.
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- Training Set:
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- Full Training Set: 1,755,602 observations, 3,307,025 images
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- Labeled Training Sets:
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- Test Set:
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- 182,618 observations, 334,887 images
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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: 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: Multiple ground-level images are paired to the same aerial image, which may require special handling in machine learning models.
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- iNaturalist: www.inaturalist.org
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- nited States Department of Agriculture: NAIP Imagery.
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sequence: int64
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# Nature Multi-View (NMV) Dataset Datacard
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To encourage development of better machine learning methods for operating with diverse, unlabeled natural world imagery, we introduce Nature Multi-View (NMV), a multi-view dataset of over 3 million ground-level and aerial image pairs from over 1.75 million citizen science observations for over 6,000 native and introduced plant species across California.
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## Dataset Overview
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- Total Images: Over 3 million ground-level and aerial image pairs
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- Species Covered: Over 6,000 native and introduced plant species
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- Geographic Focus: California, USA
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## Data Description
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- Ground-Level Images:
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- Sourced from iNaturalist open data on AWS.
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- Filters applied:
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- RGB-Infrared images, 256x256 pixels, 60 cm-per-pixel resolution.
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- Centered on the latitude and longitude of the iNaturalist observation.
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## Dataset Splits
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- Training Set:
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- Full Training Set: 1,755,602 observations, 3,307,025 images
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- Labeled Training Sets:
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- Test Set:
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- 182,618 observations, 334,887 images
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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: 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: Multiple ground-level images are paired to the same aerial image, which may require special handling in machine learning models.
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## References
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- iNaturalist: www.inaturalist.org
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- nited States Department of Agriculture: NAIP Imagery.
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