---
license: cdla-permissive-1.0
language:
- en
pretty_name: LILA BC Camera Trap Data
tags:
- biology
- image
- animals
- CV
- camera traps
size_categories:
- 1M<n<10M
---

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# Dataset Card for LILA BC Camera Trap Data

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## Dataset Description

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- **Homepage:** 
- **Repository:** [related project repo]
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

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This dataset contains the LILA BC full camera trap information with notebook ([`lilabc_CT.ipynb`](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/main/notebooks/lilabc_CT.ipynb)) exploring available data. The last run of this (in [commit 010ecf0](https://huggingface.co/datasets/imageomics/lila-bc-camera/commit/010ecf0c6a2e0c99c9481cea793d8b1556b5c71e)) uses and produces the lila CSVs found [here](https://huggingface.co/datasets/imageomics/lila-bc-camera/tree/010ecf0c6a2e0c99c9481cea793d8b1556b5c71e/data).
More details on this are below in [Data Instances](#data-instances).

Looks at potential test sets constructed from 7 different LILA datasets (uses [data/potential-test-sets/lila_image_urls_and_labels.csv](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/37b93ddf25c63bc30d8488ef78c1a53b9c4a3115/data/potential-test-sets/lila_image_urls_and_labels.csv) (sha256:3fdf87ceea75f8720208a95350c3c70831a6c1c745a92bb68c7f2c3239e4c455) to separate them out):
We're specifically interested in the following datasets identified in the [spreadsheet](https://docs.google.com/spreadsheets/d/1sC90DolAvswDUJ1lNSf0sk_norR24LwzX2O4g9OxMZE/edit?usp=drive_link) as labeled at the image-level.
  - [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/)
  - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)
  - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)
  - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)
  - [SWG Camera Traps 2018-2020](https://lila.science/datasets/swg-camera-traps)
  - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)
  - [ENA24-detection](https://lila.science/datasets/ena24detection)

There are 2,867,312 images in this subset (once humans and non-creatures are removed).

[NOAA Puget Sound Nearshore Fish 2017-2018](https://lila.science/datasets/noaa-puget-sound-nearshore-fish) could be interesting for the combined categories, though it is _very_ general (has only three labels: `fish`, `crab`, `fish_and_crab`). It also isn't included in the CSV, so not explored further.

More details on this provided in [Test Data Instances](#test-data-instances), below.

**Repo file description at [commit 87e2e4d](https://huggingface.co/datasets/imageomics/lila-bc-camera/tree/87e2e4d46cf1e8daadd74b7738856a1e30754de3) when we were considering it for BioCLIP v1 testing:**

Images have been deduplicated and reduced down to species designation, with the main CSV filtered to just those with species labels and only one animal per image. This was done by pulling the first instance of an animal so that there are not repeat images of the same animal from essentially the same time.

The deduplicated collection ([lila_image_urls_and_labels_species.csv](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/f2d596714c46bf30edf1f45efe88b3a09b3c5f81/data/lila_image_urls_and_labels_species.csv)) has 6,365,985 images (compared to the full dataset of 16,833,848 at time of download). Its [associated taxonomy mapping release](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/f2d596714c46bf30edf1f45efe88b3a09b3c5f81/data/lila-taxonomy-mapping_release.csv).

See the [LILA BC HF Dataset](https://huggingface.co/datasets/society-ethics/lila_camera_traps) for more inforamtion and updated data.



### Supported Tasks and Leaderboards
[More Information Needed]

### Languages
[More Information Needed]

## Dataset Structure

```
/dataset/
    data/
        lila-taxonomy-mapping_release.csv
        lila_image_urls_and_labels.csv
        lila_image_urls_and_labels_species.csv    # Outdated
        lila_image_urls_and_labels_wHumans.csv
        potential-test-sets/
            lila-taxonomy-mapping_release.csv
            lila_image_urls_and_labels.csv
                filtered/
                    ENA24-imbalanced.csv
                    ENA24-balanced.csv
                    ENA24-balanced-small.csv
                    desert-lion-upper-lower-bound.csv
                    desert-lion-upper-bound.csv
                    desert-lion-balanced.csv
                    island-lower-bound_common.csv
                    island-lower-bound_family.csv
                    island-imbalanced_family.csv
                    island-balanced.csv
                    island-imbalanced_common.csv
                    ohio-small-animals-upper-lower-bound.csv
                    ohio-small-animals-upper-bound.csv
                    ohio-small-animals-balanced.csv
                    orinoquia-upper-lower-bound.csv
                    orinoquia-upper-bound.csv
                    orinoquia-balanced.csv
    notebooks/
        lilabc_CT.ipynb
        lilabc_CT.py
        lilabc_test-<dataset_name>.ipynb
        lilabc_test-EDA.py
        lilabc_test-filter.ipynb
        lilabc_test-filter.py
```

**Notes:**
 - `dataset_name` is one of `desert-lion`, `ENA24`, `island`, `ohio-small-animal`, or `orinoquia`. Each collection of `<dataset_name>-<size_indicator>` CSVs are created in their corresponding `lilabc_test-<dataset_name>` notebook.
 - All the "balanced" datasets and `ENA24-balanced-small.csv` have 12 images per species (or family, in the case of the island-balanced CSV). `ENA24-balanced.csv` has 56 images per species.
 - `upper-bound` are max 10K images per species, with no minimum (this often means the smallest classification class has just 1 image).
 - `upper-lower-bound` CSVs are max 10K images per species and minimum of 10.
 - ENA24 has a minimum of 56 images per species and a maximum of 893, so `ENA24-imbalanced.csv` is just all images containing a single species.
 - The island camera traps were mostly only labeled to family level, so there are common name and family versions. The `imbalanced` sets are just all images with common name or family designation, respectively. The `lower-bound` are only those with at least ten images per class (by common name and family), and `balanced` is just 12 images per family.


### Data Instances

The [`data/lila_image_urls_and_labels.csv`](https://huggingface.co/datasets/imageomics/lila-bc-camera/blob/010ecf0c6a2e0c99c9481cea793d8b1556b5c71e/data/lila_image_urls_and_labels.csv) has all images with non-taxa (identified by `scientific_name`, `common_name`, and `kingdom` are null) or `human` original labels filtered out and has 10,104,328 images.
7,521,712 have full 7-rank taxa, with 891 unique 7-tuple strings (908 unique including subranks), with 890 unique scientific names -- this count is from before humans were removed (there are 257,159 images with humans listed and they do have full 7-rank taxa).
Final version at this stage has 9,849,119 images, 907 unique scientific names.

**annotation_level**
```
sequence    4156306
image       2892394
unknown     2886844
```

**non-taxa labels:**
```
original_label
problem                   288579
blurred                   184620
ignore                    177546
vehicle                    26445
unknown                    26170
snow on lens               17552
foggy lens                 15832
vegetation obstruction      6994
malfunction                 5640
unclassifiable              3484
motorcycle                  3423
misdirected                 2832
other                       2474
unidentifiable              1472
foggy weather               1380
lens obscured                866
sun                          835
end                          616
fire                         578
misfire                      400
eye_shine                    328
start                        321
tilted                        56
unidentified                  39
```

**Datasets with the non-taxa labels:**
```
dataset_name
SWG Camera Traps                    650745
Idaho Camera Traps                   66339
NACTI                                26015
WCS Camera Traps                     18320
Wellington Camera Traps               3484
Orinoquia Camera Traps                1280
Island Conservation Camera Traps      1269
Snapshot Serengeti                     568
ENA24                                  293
Channel Islands Camera Traps           159
Snapshot Mountain Zebra                  7
Snapshot Camdeboo                        3
```

### Test Data Instances

**data/potential-test-sets/lila_image_urls_and_labels.csv:** Reduced down to the datasets of interest listed below; all those with `original_label` "empty" or null `scientific_name` (these had non-taxa labels) were removed. 
Additionally, added a `multi_species` column (boolean to indicate multiple species are present in the image--it gets listed once for each species in the image) and a count of how many different species are in each of those images (`num_species` column).

There are 367 unique scientific names in this subset (355 by full 7-rank), 184 unique among just those labeled at the image-level (180 by full 7-rank) (as indicated by the CSV). 
This was then subdivided into CSVs for each of the target datasets (`data/potential-test-sets/<dataset_name>_image_urls_and_labels.csv`).
These were initially identified from our [master spreadsheet](https://docs.google.com/spreadsheets/d/1sC90DolAvswDUJ1lNSf0sk_norR24LwzX2O4g9OxMZE/edit?gid=0#gid=0), identifying image-level labeled datasets and those that are a meaningful measure of our biodiversity-focused model (e.g., includes rare species--those less-commonly seen, targeting areas with greater biodiversity).

  - [Snapshot Safari 2024 Expansion](https://lila.science/datasets/snapshot-safari-2024-expansion/) -- actually labeled by sequence, so not a good choice for testing
  - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)
  - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)
  - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)
  - [SWG Camera Traps 2018-2020](https://lila.science/datasets/swg-camera-traps) -- actually labeled by sequence, so not a good choice for testing
  - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)
  - [ENA24-detection](https://lila.science/datasets/ena24detection)

Multi-species counts (full):
```
num_species
1.0     2753832
2.0      114825
3.0       13995
4.0        1704
5.0         230
14.0         42
```
For Image-level labels:
```
num_species
1.0    305821
2.0      1154
3.0         3
```
Looks like we'll have about 306K images across the 5 datasets that have image-level labels.



### Data Fields
[More Information Needed]
<!--
Describe the types of the data files or the columns in a CSV with metadata.
-->

Each of the `<dataset_name>_<type>` CSVs has the following columns.

 - `dataset_name`: name of the LILA BC dataset
 - `url_gcp`, `url_aws`, `url_azure` are URLs to potentially access the image, we recommend `url_aws`.
 - `image_id`: unique identifier for the image.
 - `sequence_id`: ID of the sequence to which the image belongs.
 - `location_id`: ID of the location at which the camera was placed.
 - `frame_num`: generally 0, 1, or 2, indicates order of image within a sequence.
 - `original_label`: label initially assigned to the image.
 - `scientific_name`: genus species of the animal in the image. For the island CSV, lowest rank taxa available, generally family. 
 - `common_name`: vernacular name of the animal in the image. For the island CSV, this is generally for the family, but it's a mix.
 - `kingdom`: kingdom of the animal in the image.
 - `phylum`: phylum of the animal in the image.
 - `class`: class of the animal in the image.
 - `order`: order of the animal in the image.
 - `family`: family of the animal in the image.
 - `genus`: genus of the animal in the image. About half null in the island CSVs.
 - `species`: species of the animal in the image. Mostly null in the island CSVs.
 - `num_sp_images`: number of images of that species in the dataset.
 For the island CSVs, instead of `num_sp_images` there are `num_fam_images` and `num_cn_images` representing the number of images for the family or common name, respectively.

Additionally, the `ohio-small-animals` CSVs have a `filename` column defined as `OH_sm_animals_<filename in url_aws>`.


### Data Splits
[More Information Needed]
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Give your train-test splits for benchmarking
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## Dataset Creation

### Curation Rationale
[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization
[More Information Needed]

#### Who are the source language producers?
[More Information Needed]

### Annotations

#### Annotation process
[More Information Needed]

#### Who are the annotators?
[More Information Needed]

### Personal and Sensitive Information
[More Information Needed]

<!-- For instance, if your data includes people or endangered species. -->

## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]

### Discussion of Biases
[More Information Needed]

### Other Known Limitations
[More Information Needed]

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## Additional Information

### Dataset Curators
Elizabeth Campolongo

### Licensing Information

This compilation is licensed under the [Community Data License Agreement (permissive variant)](https://cdla.io/permissive-1-0/), same as the images and metadata which belong to their original sources (see citation directions below).

### Citation Information

For test sets (provided citations on their LILA BC pages are included):

  - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)
    - Balasubramaniam S. [Optimized Classification in Camera Trap Images: An Approach with Smart Camera Traps, Machine Learning, and Human Inference](https://etd.ohiolink.edu/acprod/odb_etd/etd/r/1501/10?clear=10&p10_accession_num=osu1721417695430687). Master’s thesis, The Ohio State University. 2024.
  - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)
  - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)
    - Vélez J, McShea W, Shamon H, Castiblanco‐Camacho PJ, Tabak MA, Chalmers C, Fergus P, Fieberg J. [An evaluation of platforms for processing camera‐trap data using artificial intelligence](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14044). Methods in Ecology and Evolution. 2023 Feb;14(2):459-77.
  - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)
  - [ENA24-detection](https://lila.science/datasets/ena24detection)
    - Yousif H, Kays R, Zhihai H. Dynamic Programming Selection of Object Proposals for Sequence-Level Animal Species Classification in the Wild. IEEE Transactions on Circuits and Systems for Video Technology, 2019. ([bibtex](http://lila.science/wp-content/uploads/2019/12/hayder2019_bibtex.txt))

[More Information Needed]
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### Contributions

The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118240) (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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