andyvhuynh/NatureMultiView

[image]

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).
• —glimagedate (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.
• —glinatid (string): iNaturalist identifier for the ground-level observation.
• —glphotoid (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.
• —rsimagedate (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.