|
--- |
|
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. |
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|
