---
dataset_info:
  features:
  - dtype: string
    name: observation_uuid
  - dtype: float32
    name: latitude
  - dtype: float32
    name: longitude
  - dtype: int64
    name: positional_accuracy
  - dtype: int64
    name: taxon_id
  - dtype: string
    name: quality_grade
  - dtype: string
    name: gl_image_date
  - dtype: string
    name: ancestry
  - dtype: string
    name: rank
  - dtype: string
    name: name
  - dtype: string
    name: gl_inat_id
  - dtype: int64
    name: gl_photo_id
  - dtype: string
    name: license
  - dtype: string
    name: observer_id
  - dtype: bool
    name: rs_classification
  - dtype: string
    name: ecoregion
  - dtype: bool
    name: supervised
  - dtype: string
    name: rs_image_date
  - dtype: bool
    name: finetune_0.25percent
  - dtype: bool
    name: finetune_0.5percent
  - dtype: bool
    name: finetune_1.0percent
  - dtype: bool
    name: finetune_2.5percent
  - dtype: bool
    name: finetune_5.0percent
  - dtype: bool
    name: finetune_10.0percent
  - dtype: bool
    name: finetune_20.0percent
  - dtype: bool
    name: finetune_100.0percent
  - dtype: image
    name: gl_image
  - name: rs_image
    sequence:
      sequence:
        sequence: int64
---

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.