Update README.md

Added Chuck's updates

README.md

@@ -27,16 +27,17 @@ configs:
   - final_metadata.json
 description: >-
   A comprehensive ecological computer vision dataset designed for wildlife
-  detection, species classification, and individual animal re-identification.
-  Features bounding boxes, identifiability scores, and individual re-ID clusters
+  detection, species classification, and especially individual animal re-identification.
+  The images were collected during the Great Grevy's Rally in 2016 and 2018 in Meru County, Kenya.
+  The dataset features bounding boxes, identifiability scores, and individual re-ID clusters
   processed through the 9-stage Great Grevy's Rally (GGR) pipeline.
 ---
 
 # Dataset Card for GZCD (Grevy's Zebra Census Dataset)
 
-The **GZCD (Grevy's Zebra Census Dataset)** is a comprehensive ecological computer vision dataset designed for wildlife detection, species classification, and individual animal re-identification (Re-ID). Captured primarily in the field, this dataset emphasizes endangered equid and ungulate populations, specifically Grevy's zebras, Plains zebras, and Reticulated giraffes. 
+The **GZCD (Grevy's Zebra Census Dataset)** is a comprehensive ecological computer vision dataset designed for wildlife detection, species classification, and individual animal re-identification (Re-ID). Captured entirely during the fieldwork, this dataset emphasizes endangered equid and ungulate populations, specifically Grevy's zebras, Plains zebras, and Reticulated giraffes. 
 
-GZCD consists of field photographs captured in Meru County, Kenya, taken over four days by 13 photographers during 2016 and 2018 iterations of the **Great Grevy's Rally (GGR)**. The data collection was carried out by citizen scientists and conservationists across a 25,000 square kilometer range. It was curated to help train and test new computer vision algorithms, most notably building upon the pivotal animal re-identification work by Jason Parham.
+GZCD consists of field photographs captured in Meru County, Kenya, taken over four days by 13 photographers during 2016 and 2018 iterations of the **Great Grevy's Rally (GGR)**. The data collection was carried out by citizen scientists and conservationists across a 25,000 square kilometer range. It was curated to help train and test new computer vision algorithms, originally as part of Jason Parham's PhD dissertation work (Parham, 2021; Parham et al., 2017) and refined by a team at the Imageomics Institute.
 
 ---
 
@@ -56,7 +57,6 @@ This dataset provides raw ecological image data alongside the carefully processe
 The dataset contains the raw images and two metadata files representing the data before and after processing through the GGR pipeline.
 
 ```
-text
 /dataset/
     images/
         <uuid 1>.jpg
@@ -66,7 +66,6 @@ text
     final_metadata.json
 ```
 
-
 ### Data Instances
 - `images/`: The raw field images capturing various wildlife species.
 - `initial_metadata.json`: Represents the raw state of the dataset prior to processing, containing EXIF data, camera body details, flash card source data, GPS coordinates, and initial image dimensions.
@@ -76,42 +75,26 @@ text
 
 #### Categories
 Both JSON files share a consistent mapping of 23 categories: 
-* `0: grevy's zebra`
-* `1: plains zebra`
-* `2: giraffe_reticulated`
-* `3: ignore`
-* `4: bird`
-* `5: antelope`
-* `6: impala`
-* `7: goat`
-* `8: water_buffalo`
-* `9: person`
-* `10: gazelle`
-* `11: lion`
-* `12: donkey`
-* `13: domesticated_cow`
-* `14: rhino_white`
-* `15: elephant_savanna`
-* `16: horse`
-* `17: ostrich`
-* `18: warthog`
-* `19: car`
-* `20: rhino_black`
-* `21: camel`
-* `22: ____`
+| category ID : category Name | category ID : category Name | category ID : category Name | category ID : category Name | category ID : category Name |
+| :--- | :--- | :--- | :--- | :--- |
+| 0 : grevy's zebra | 1 : plains zebra | 2 : giraffe_reticulated | 3 : ignore | 4 : bird |
+| 5 : antelope | 6 : impala | 7 : goat | 8 : water_buffalo | 9 : person |
+| 10 : gazelle | 11 : lion | 12 : donkey | 13 : domesticated_cow | 14 : rhino_white |
+| 15 : elephant_savanna | 16 : horse | 17 : ostrich | 18 : warthog | 19 : car |
+| 20 : rhino_black | 21 : camel | 22 : \_\_\_\_ | | |
 
 
 #### `initial_metadata.json`
 Contains metadata of the image files before running the pipeline:
 - **`gid`**: Integer global ID.
-- **`uuid`**: Unique string identifier for the image.
+- **`uuid`**: Unique string identifier for the image. This is computed deterministically so it will not change unless the contents of the file are changed.
 - **`uri` / `uri_original` / `original_name` / `original_path`**: File paths and names pointing to the `images/` directory.
 - **`ext`**: File extension (e.g., `.jpg`).
 - **`width` / `height`**: Image dimensions in pixels.
-- **`time_posix`**: Epoch timestamp of image capture.
+- **`time_posix`**: Posix time of image capture. Cameras were time-synchronized at the start of each image collection drive.
 - **`gps_lat` / `gps_lon`**: Geographic coordinates of the image capture. *Note: Missing, masked, or unknown GPS coordinates are explicitly encoded as `-1.0`.*
-- **`orientation`**: Camera orientation integer.
-- **`location_code`**: Dataset specific location code (e.g., "GZCD").
+- **`orientation`**: Camera orientation integer. This represents the standard EXIF orientation tag (values 1 through 8) embedded by the camera. It tells the software which way is "up," ensuring the image isn't viewed sideways or upside-down. For example, 1 is standard/normal, 3 is upside-down, and 6 or 8 represent 90-degree rotations.
+- **`location_code`**: A short categorical label identifying which data-collection campaign or site an image belongs to (e.g., `"GZCD"` for this dataset). It lets images from different collection events or geographic areas be grouped and filtered consistently throughout the pipeline.
 - **`camera_make` / `camera_model`**: Hardware metadata extracted from image EXIF data.
 - **`card_id`**: Identifier string tracking the specific physical storage card used during field data ingestion.
 
@@ -120,46 +103,46 @@ Contains metadata of the image files before running the pipeline:
 Contains cleaned image metadata and a rich `annotations` array generated by the GGR pipeline:
 - **Image Data:** `uuid`, `image_path`, `datetime`, `latitude`, `longitude` (encoded as `-1.0` if unknown), `width`, `height`, `camera_make`, `camera_model`, `card_id`.
 - **Annotations:**
-  - **`image_uuid`**: Links the annotation to the parent image.
-  - **`bbox`**: Absolute bounding box `[x, y, width, height]` of the detected animal.
-  - **`region_bbox`**: A localized bounding box of census annotation region.
-  - **`category_id`**: Integer corresponding to the species category list.
-  - **`viewpoint`**: String denoting viewpoint orientation (e.g., `right`, `left`).
-  - **`CA_score`**: Float representing the Identifiable Annotation (IA) confidence score.
-  - **`annotations_census`**: Boolean indicating if the annotation is valid for population counting.
+  - **`image_uuid`**: The `uuid` of the parent image this annotation was drawn on (i.e., the image-level `uuid` described in the Image Data section above). It links each detection back to its source image.
+  - **`bbox`**: Bounding box of the detected animal in **absolute pixel coordinates**, as `[x, y, width, height]`. `(x, y)` is the **top-left (upper-left) corner** of the box, measured from the image's top-left origin (x increases rightward, y increases downward); `width` and `height` extend right and down from that corner.
+  - **`region_bbox`**: The **Census Annotation Region (CA-R)** — a tighter box, in the same pixel format as `bbox`, that crops down to just the most identifiable part of the animal (for Grévy's zebra, the hip and shoulder chevron stripe pattern). Its purpose is to remove distracting background and body parts so that individual re-identification matches on the most discriminative region, which makes matching faster and more accurate.
+  - **`category_id`**: Integer index into the species category list defined in [Categories](#categories) section. It maps the annotation to its species (e.g., grevy's zebra).
+  - **`viewpoint`**: String giving the side of the animal shown. Possible values follow the WBIA viewpoint vocabulary: `left`, `right`, `front`, `back`, `frontleft`, `frontright`, `backleft`, `backright`. Because GZCD photographers were trained to shoot the right side, annotations are overwhelmingly `right`, `frontright`, or `backright`.
+  - **`CA_score`**: The Census Annotation confidence score — a float from **0.0 to 1.0** output by the CA classifier, representing how confident the model is that the annotation is a clean, universally-comparable view usable for individual identification (correct side, with the identifying chevron pattern clearly visible). Higher is better: ~0.99 means a clear, high-quality view; values near 0 mean the chevron is not clearly visible.
+  - **`annotations_census`**: Boolean indicating whether this annotation qualifies as a Census Annotation — i.e., it passed the census filtering (correct species/viewpoint and a high enough `CA_score`) and is therefore considered valid for inclusion in the population count. Restricting counting to these clean views avoids over-counting from poor or ambiguous detections.
   - **`annot_uuid`**: Unique identifier for the specific detection.
-  - **`manual`**: Boolean indicating if the annotation underwent manual verification.
-  - **`individual_id`**: UUID grouping annotations belonging to the exact same individual animal across different images for Re-ID.
+  - **`manual`**: Boolean indicating whether a human reviewer set or verified this annotation. `true` means a person confirmed (or hand-corrected) the box and its labels; `false` means it was produced automatically by the detection pipeline without human review.
+  - **`individual_id`**: UUID that indicates which individual animal is seen in this annotation. All annotations that were determined to be from the same individual animal will have the same, unique UUID (used for Re-ID across images).
 
 ---
 
 ## Dataset Creation
 
 ### Data Collection and Processing
-The images were collected in **Meru County, Kenya** during the 2016 and 2018 census rallies. This dataset is the direct output of the GGR Pipeline, a 9-stage workflow designed for robust ecological tracking:
+The images were collected in **Meru County, Kenya** during the 2016 and 2018 census rallies. This dataset is the output of the GGR Pipeline, a `8-stage` workflow designed for robust ecological tracking. Some manual corrections were made. Here are the stages:
 
-1. **Import:** Parses raw field data, ingests card/camera metadata, and generates standard metadata descriptions, mapping absolute paths to the images.
+1. **Import:** Parses raw field data, ingests card/camera metadata, and generates standard metadata descriptions, mapping absolute paths to the images. Synchronizations time and GPS recording with external sources.
 2. **Detection:** Utilizes YOLO to localize animals in the images, creating initial bounding boxes (`bbox`).
 3. **Species Classification:** Processes detections through BioCLIP to classify the species (e.g., Grevy's zebra vs. Plains zebra).
-4. **Viewpoint Classification:** Determines the orientation of the detected animal (`up`, `front`, `back`, `left`, `right`).
-5. **Census Annotation (CA) Classification:** Evaluates photographic quality and pose. Assigns a `CA_score` and a boolean flag indicating if the animal's features (e.g., stripes) are clear enough for individual identification.
-6. **CA Filtering:** Discards annotations below the identifiability threshold decided empirically and simplifies viewpoints strictly to `left` or `right` flanks for Re-ID purposes.
-7. **MiewID:** Generates deep metric embeddings for all filtered high-quality annotations leveraging the MiewID feature extraction model.
-8. **Local Clusters and Alternatives (LCA) Algorithm:** Clusters the generated embeddings based on feature similarity, grouping annotations of the same individual animal together by resolving inconsistencies.
+4. **Viewpoint Classification:** Determines the orientation of the detected animal (`left`, `frontleft`, `back`, `right`, `backright` etc).
+5. **Census Annotation (CA) Classification:** Evaluates photographic quality and pose. Assigns a `CA_score` and a boolean flag indicating if the annotation shows a clear enough view of the animal's right hip and right chevron.
+6. **CA Filtering:** Discards annotations below an identifiability threshold decided empirically and simplifies viewpoints strictly to `left` or `right` flanks for Re-ID purposes.
+7. **MiewID:** Generates deep metric embeddings for all CA's the MiewID model (Otarashvili, 2023).
+8. **Local Clusters and Alternatives (LCA) Algorithm:** Generates clusters of CA's each corresponding to a single animal. Uses human-in-the-loop decision making to resolve inconsistencies.
 ---
 
 ## Considerations for Using the Data
 
 - **Missing Location Data:** When GPS coordinates could not be resolved from field equipment or manual logs, fields for `latitude` and `longitude` (`gps_lat`/`gps_lon`) default to `-1.0`. Algorithms utilizing geometric filtering should catch these sentinel values.
-- **Viewpoint Filtering:** Because Re-ID of Grevy's Zebra relies on right-flank stripe patterns, the pipeline aggressively selects `right` viewpoints whose embeddings are extracted and ID assigned. Other viewpoints are excluded from ID assignment.
-- **Geographic Focus:** The spatial data anchors tightly to the **Meru County** ecosystem in Kenya. Models trained heavily on this dataset may experience domain shift when deployed in different biomes or under disparate lighting conditions.
+- **Viewpoint Filtering:** Because the GGR photographers were strongly encouraged to take images showing a zebra's right hip and chevron, there is a strong bias in the dataset toward right side viewpoint. 
+- **Geographic Focus:** The imageset is collected exclusively in **Meru County** in Kenya. Models trained exclusively on this dataset may experience issues associated with domain shift when deployed in different environments.
 - **Class Imbalance:** Due to the targeted focus of the Great Grevy's Rally on Grevy's Zebras, the final JSON contains individual IDs for Grevy's Zebra only.
 
 ---
 
 ## Citations & References
 
-If you use this dataset, please cite both the dataset release and the foundational computer vision framework developed for scaling up citizen science wildlife censuses:
+If you use this dataset, please cite the dataset release along with the foundational computer vision work that scaled up citizen-science wildlife censusing (the GGR pipeline and Parham's dissertation) and the MiewID re-identification model used in the pipeline:
 
 ```bibtex
 @misc{gzcd2026,
@@ -167,16 +150,34 @@ If you use this dataset, please cite both the dataset release and the foundation
   title = {GZCD: Grevy's Zebra Census Dataset},
   year = {2026},
   publisher = {Hugging Face},
-  howpublished = {\url{[https://huggingface.co/datasets/imageomics/GZCD](https://huggingface.co/datasets/imageomics/GZCD)}}
+  howpublished = {\url{https://huggingface.co/datasets/imageomics/GZCD}}
 }
 ```
+```bibtex
+@phdthesis{parham2021animal,
+  author = {Jason R. Parham},
+  title = {Animal Detection for Photographic Censusing},
+  school = {Rensselaer Polytechnic Institute},
+  address = {Troy, NY, USA},
+  year = {2021}
+}
 ```
+```bibtex
 @inproceedings{parham2017animal,
-  author    = {Jason Parham and Jonathan Crall and Charles Stewart and Tanya Berger-Wolf and Daniel I. Rubenstein},
-  title     = {Animal Population Censusing at Scale with Citizen Science and Photographic Identification},
+  author = {Jason Parham and Jonathan Crall and Charles Stewart and Tanya Berger-Wolf and Daniel I. Rubenstein},
+  title = {Animal Population Censusing at Scale with Citizen Science and Photographic Identification},
   booktitle = {Proceedings of the AAAI 2017 Spring Symposium on AI for Social Good},
-  address   = {Stanford, CA},
-  year      = {2017}
+  address = {Stanford, CA},
+  year = {2017}
+}
+```
+```bibtex
+@misc{otarashvili2023miewid,
+  author = {Lasha Otarashvili},
+  title = {MiewID},
+  year = {2023},
+  doi = {10.5281/zenodo.13647526},
+  url = {https://github.com/WildMeOrg/wbia-plugin-miew-id}
 }
 ```