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README.md

@@ -60,28 +60,16 @@ dataset_info:
       sequence:
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 ---
-![NMV Dataset Overview](nmv_overview.png)
 
 # 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
+## 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
 
-## Acquisition
+## Data Description
 - Ground-Level Images:
   - Sourced from iNaturalist open data on AWS.
   - Filters applied:
@@ -96,38 +84,25 @@ 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
 
-## 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.
+## 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.
 
 ## References
 - iNaturalist: www.inaturalist.org
-- United States Department of Agriculture: NAIP Imagery. www.naip-usdaonline.hub.arcgis.com.
-
+- United States Department of Agriculture: NAIP Imagery. naip-usdaonline.hub.arcgis.com.

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