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README.md

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+Nature Multi-View (NMV) Dataset Datacard
+Dataset Overview
+
+Name: Nature Multi-View Dataset (NMV)
+Total Observations: 3,921,026
+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.
+Self-Supervised Learning: Designed to encourage the development of self-supervised machine learning methods, unrestricted by label balance.