|
--- |
|
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 |
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|
|
## 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. |
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- Many-to-One Pairing: Multiple ground-level images are paired to the same aerial image, which may require special handling in machine learning models. |
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|
|
## References |
|
- iNaturalist: www.inaturalist.org |
|
- nited States Department of Agriculture: NAIP Imagery. |
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|
