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.