Update README.md

README.md

@@ -64,12 +64,25 @@ dataset_info:
 # 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
+## 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 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 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
 
-## Data Description
+## Acquisition
 - Ground-Level Images:
   - Sourced from iNaturalist open data on AWS.
   - Filters applied:
@@ -84,25 +97,38 @@ To encourage development of better machine learning methods for operating with d
   - 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.
+## 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, measured in meters.
+- 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. naip-usdaonline.hub.arcgis.com.
+- United States Department of Agriculture: NAIP Imagery. www.naip-usdaonline.hub.arcgis.com.
+