File size: 3,174 Bytes
b529587 5640a3d b529587 e9c4bbc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
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.
|