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IDLE-OO-Camera-Traps

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metadata
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

Dataset Card for LILA BC Camera Trap Data

Dataset Description

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

Dataset Summary

This dataset contains the LILA BC full camera trap information with notebook (lilabc_CT.ipynb) exploring available data. The last run of this (in commit 010ecf0) uses and produces the lila CSVs found here. More details on this are below in Data Instances.

Looks at potential test sets constructed from 7 different LILA datasets (uses 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 as labeled at the image-level.

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

NOAA Puget Sound Nearshore Fish 2017-2018 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, below.

Repo file description at commit 87e2e4d 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) has 6,365,985 images (compared to the full dataset of 16,833,848 at time of download). Its associated taxonomy mapping release.

See the LILA BC HF Dataset 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        lila_image_urls_and_labels_species.csv    # Outdated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 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, 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).

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]

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]

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]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Elizabeth Campolongo

Licensing Information

This compilation is licensed under the Community Data License Agreement (permissive variant), 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):

[More Information Needed]

Contributions

The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #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.