Search is not available for this dataset
observation_uuid
string | latitude
float32 | longitude
float32 | positional_accuracy
int64 | taxon_id
int64 | quality_grade
string | gl_image_date
string | ancestry
string | rank
string | name
string | gl_inat_id
string | gl_photo_id
int64 | license
string | observer_id
string | rs_classification
bool | ecoregion
string | supervised
bool | rs_image_date
string | finetune_0.25percent
bool | finetune_0.5percent
bool | finetune_1.0percent
bool | finetune_2.5percent
bool | finetune_5.0percent
bool | finetune_10.0percent
bool | finetune_20.0percent
bool | finetune_100.0percent
bool | gl_image
image | rs_image
sequence |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c3bfe327-c038-4f12-9d64-b6637ca6ba59 | 37.773072 | -122.46238 | 10 | 54,854 | needs_id | 2019-06-02 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | 9acde7f2-5450-4212-9403-3b8c9ad2ecd7 | 40,796,789 | CC-BY-NC | 32269 | true | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[141,140,142,140,132,126,128,122,138,167,165,144,136,134,133,134,132,133,134,139,140,143,143,143,1(...TRUNCATED) |
|
c3bfe327-c038-4f12-9d64-b6637ca6ba59 | 37.773072 | -122.46238 | 10 | 54,854 | needs_id | 2019-06-02 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | e3fd0417-4dac-4dfe-a6e8-275ee6c6268e | 40,796,831 | CC-BY-NC | 32269 | false | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[141,140,142,140,132,126,128,122,138,167,165,144,136,134,133,134,132,133,134,139,140,143,143,143,1(...TRUNCATED) |
|
c3bfe327-c038-4f12-9d64-b6637ca6ba59 | 37.773072 | -122.46238 | 10 | 54,854 | needs_id | 2019-06-02 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | 53f13435-598b-4e1a-a309-8e7e94f84148 | 40,796,763 | CC-BY-NC | 32269 | false | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[141,140,142,140,132,126,128,122,138,167,165,144,136,134,133,134,132,133,134,139,140,143,143,143,1(...TRUNCATED) |
|
c3bfe327-c038-4f12-9d64-b6637ca6ba59 | 37.773072 | -122.46238 | 10 | 54,854 | needs_id | 2019-06-02 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | ff62f0bb-b7f7-4081-8d9b-c4fbd9347837 | 40,796,813 | CC-BY-NC | 32269 | false | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[141,140,142,140,132,126,128,122,138,167,165,144,136,134,133,134,132,133,134,139,140,143,143,143,1(...TRUNCATED) |
|
32a1215c-2c1a-4e9a-bec6-b9420c538bbf | 37.77303 | -122.462372 | 10 | 54,854 | research | 2021-07-10 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | d0da6a4d-a4d2-404f-82f4-dad8c878e5d7 | 142,504,863 | CC-BY-NC | 32269 | true | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[54,53,55,78,139,143,145,148,145,148,147,151,152,154,159,163,170,177,179,182,180,177,170,166,161,1(...TRUNCATED) |
|
32a1215c-2c1a-4e9a-bec6-b9420c538bbf | 37.77303 | -122.462372 | 10 | 54,854 | research | 2021-07-10 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | 22d72627-7afd-42be-ac56-573b11e2ce3f | 142,504,877 | CC-BY-NC | 32269 | false | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[54,53,55,78,139,143,145,148,145,148,147,151,152,154,159,163,170,177,179,182,180,177,170,166,161,1(...TRUNCATED) |
|
32a1215c-2c1a-4e9a-bec6-b9420c538bbf | 37.77303 | -122.462372 | 10 | 54,854 | research | 2021-07-10 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | 67629bb9-fcb7-489c-8a90-c22668c7e2f4 | 142,504,903 | CC-BY-NC | 32269 | false | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[54,53,55,78,139,143,145,148,145,148,147,151,152,154,159,163,170,177,179,182,180,177,170,166,161,1(...TRUNCATED) |
|
32a1215c-2c1a-4e9a-bec6-b9420c538bbf | 37.77303 | -122.462372 | 10 | 54,854 | research | 2021-07-10 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | 9301f43e-b6c2-4049-aae2-0c02ed63926c | 142,504,947 | CC-BY-NC | 32269 | false | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[54,53,55,78,139,143,145,148,145,148,147,151,152,154,159,163,170,177,179,182,180,177,170,166,161,1(...TRUNCATED) |
|
32a1215c-2c1a-4e9a-bec6-b9420c538bbf | 37.77303 | -122.462372 | 10 | 54,854 | research | 2021-07-10 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | b1edc8e8-9cfe-4ba8-90ba-a14503ebcb6a | 143,259,390 | CC-BY-NC | 32269 | false | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[54,53,55,78,139,143,145,148,145,148,147,151,152,154,159,163,170,177,179,182,180,177,170,166,161,1(...TRUNCATED) |
|
c2aeab1c-b9f1-4331-a36b-b24d54e4e1ae | 37.773102 | -122.462198 | 9 | 54,854 | research | 2021-08-12 | 48460/47126/211194/47125/47124/48796/48797/924978/54856 | species | Tilia americana | aac72728-f0a6-4c64-8590-9f3e26a96997 | 150,352,857 | CC-BY-NC | 28037 | true | California Coastal Sage, Chaparral, and Oak Woodlands | false | 20180804 | false | false | false | false | false | false | false | false | [[[151,165,144,148,150,151,151,149,147,147,149,149,147,145,143,142,139,138,137,137,138,133,134,135,1(...TRUNCATED) |
End of preview. Expand
in Dataset Viewer.
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.
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: The dataset 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: There are instances in the dataset where multiple ground-level images are paired to the same aerial image.
Splits
- Training Set:
- Full Training Set: 1,755,602 observations, 3,307,025 images
- Labeled Training Set:
- 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
Acquisition
- 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.
Features
- observation_uuid (string): Unique identifier for each observation in the dataset.
- latitude (float32): Latitude coordinate of the observation.
- longitude (float32): Longitude coordinate of the observation.
- positional_accuracy (int64): Accuracy of the geographical position.
- taxon_id (int64): Identifier for the taxonomic classification of the observed species.
- quality_grade (string): Quality grade of the observation, indicating its verification status (e.g., research-grade, needs ID).
- gl_image_date (string): Date when the ground-level image was taken.
- ancestry (string): Taxonomic ancestry of the observed species.
- rank (string): Taxonomic rank of the observed species (e.g., species, genus).
- name (string): Scientific name of the observed species.
- gl_inat_id (string): iNaturalist identifier for the ground-level observation.
- gl_photo_id (int64): Identifier for the ground-level photo.
- license (string): License type under which the image is shared (e.g., CC-BY).
- observer_id (string): Identifier for the observer who recorded the observation.
- rs_classification (bool): Indicates if remote sensing classification data is available.
- ecoregion (string): Ecoregion where the observation was made.
- supervised (bool): Indicates if the observation is part of the supervised dataset.
- rs_image_date (string): Date when the remote sensing (aerial) image was taken.
- finetune_0.25percent (bool): Indicates if the observation is included in the 0.25% finetuning subset.
- finetune_0.5percent (bool): Indicates if the observation is included in the 0.5% finetuning subset.
- finetune_1.0percent (bool): Indicates if the observation is included in the 1.0% finetuning subset.
- finetune_2.5percent (bool): Indicates if the observation is included in the 2.5% finetuning subset.
- finetune_5.0percent (bool): Indicates if the observation is included in the 5.0% finetuning subset.
- finetune_10.0percent (bool): Indicates if the observation is included in the 10.0% finetuning subset.
- finetune_20.0percent (bool): Indicates if the observation is included in the 20.0% finetuning subset.
- finetune_100.0percent (bool): Indicates if the observation is included in the 100.0% finetuning subset.
- gl_image (image): Ground-level image associated with the observation.
- rs_image (sequence of sequences of int64): Aerial image data associated with the observation, represented as a sequence of pixel values.
References
- iNaturalist: www.inaturalist.org
- United States Department of Agriculture: NAIP Imagery. www.naip-usdaonline.hub.arcgis.com.
- Downloads last month
- 28,551