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---
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---
# Nature Multi-View (NMV) Dataset Datacard
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.
## Dataset Overview
- Total Images: Over 3 million ground-level and aerial image pairs
- Species Covered: Over 6,000 native and introduced plant species
- Geographic Focus: California, USA
## Data Description
- Ground-Level Images:
- Sourced from iNaturalist open data on AWS.
- Filters applied:
- Vascular plants
- Within California state boundaries
- Observations dated from January 1, 2011, to September 27, 2023
- Geographic uncertainty < 120 meters
- Research-grade or in need of ID (excluding casual observations)
- Availability of corresponding remote sensing imagery
- Overlap with bio-climatic variables
- Aerial Images:
- Sourced from the 2018 National Agriculture Imagery Program (NAIP).
- RGB-Infrared images, 256x256 pixels, 60 cm-per-pixel resolution.
- Centered on the latitude and longitude of the iNaturalist observation.
## Dataset Splits
- Training Set:
- Full Training Set: 1,755,602 observations, 3,307,025 images
- Labeled Training Sets:
- 20%: 334,383 observations, 390,908 images
- 5%: 93,708 observations, 97,727 images
- 1%: 19,371 observations, 19,545 images
- 0.25%: 4,878 observations, 4,886 images
- Validation Set:
- 150,555 observations, 279,114 images
- Test Set:
- 182,618 observations, 334,887 images
## 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: 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: Multiple ground-level images are paired to the same aerial image, which may require special handling in machine learning models.
## References
- iNaturalist: www.inaturalist.org
- United States Department of Agriculture: NAIP Imagery. naip-usdaonline.hub.arcgis.com.
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