README.md

metadata

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

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
• nited States Department of Agriculture: NAIP Imagery.