1) Data Introduction • The Wildlife Animals Images Dataset is a computer vision dataset designed for image classification and generation tasks, containing various images of wild animals.

2) Data Utilization (1) Characteristics of the Wildlife Animals Images Dataset: • The dataset includes animals with visually similar features, such as species from the canine (Canidae) and feline (Felidae) families, making it suitable for training models to distinguish between animals that are often easily confused.

(2) Applications of the Wildlife Animals Images Dataset: • Wild animal classification model training: Useful for developing deep learning-based image classifiers capable of distinguishing between animal species with high visual similarity.

Dataset Card for Dataset Name

This dataset is a port of the "Animal Image Dataset" that you can find on Kaggle. The dataset contains 60 pictures for 90 types of animals, with various image sizes. With respect to the original dataset, I created the train-test-split partitions (80%/20%) to make it compatible via HuggingFace datasets. Note. At the time of writing, by looking at the Croissant ML Metadata, the original license of the data is sc:CreativeWork. If you believe this dataset… See the full description on the dataset page: https://huggingface.co/datasets/lucabaggi/animal-wildlife.

Wildlife

## Overview

Wildlife is a dataset for object detection tasks - it contains Animals annotations for 355 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Former title: COA Wildlife Conservation List

Taiwan's unique geographical location and varied topography resulted in diverse fauna on this beautiful island. However, excessive land development and resource utilization have incessantly squeezed the space for the survival of wildlife. Wildlife conservation is not just a simple act of protection, it warrants reasonable and sustainable use of natural resources.

The Wildlife Conservation Act, enacted by Ministry of Agriculture (MOA, former as Council of Agriculture, COA), is an important legal basis for wildlife management and habitat protection. Its purpose is to maintain species diversity and ecological balance. The government and related conservation organizations have designated 17 wildlife refuges. Not only are they the subject of academic researches, they are also the indicators of environmental quality. The checklist of Taiwan (TaiCOL) lists 398 endangered, rare, and other protected species of wildlife in Taiwan. The database also provides information on these species, such as their scientific names (including authors and years), common names, and synonyms. Through Taiwan Biodiversity Information Facility (TaiBIF), the information can be shared and exchanged with other GBIF participants. Users can use keywords to link to other websites with relevant information. All these efforts will result in the circulation of information in the fields of research, education and conservation, which in turn will arouse global attention to the protection of wildlife.

This data set contains approximately 1.4M camera trap images representing around 675 species from 12 countries, making it one of the most diverse camera trap data sets available publicly. Data were provided by the Wildlife Conservation Society. The most common classes are tayassu pecari (peccary), meleagris ocellata (ocellated turkey), and bos taurus (cattle). A complete list of classes and associated image counts is available here. Approximately 50% of images are empty. We have also added approximately 375,000 bounding box annotations to approximately 300,000 of those images, which come from sequences covering almost all locations. Sequences are inferred from timestamps, so may not strictly represent bursts. Images were labeled at a combination of image and sequence level, so – as is the case with most camera trap data sets – empty images may be labeled as non-empty (if an animal was present in one frame of a sequence but not in others). Images containing humans are referred to in metadata, but are not included in the data files.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

Brisbane City Council has decided to partner with the Atlas of Living Australia (ALA). The ALA will be used to capture and manage Council’s flora and fauna data. Council will be progressively loading data onto the site. ALA is a collaborative national project that aggregates biodiversity data from multiple sources and makes it freely available and usable online.

This dataset provides a link to download wildlife (flora and fauna) data for the Brisbane region from ALA. An ALA account is needed to download data. This data could be from many data providers. Any use of the data downloaded is governed by the terms and conditions specified on ALA, please read these to confirm attribution for any data used.

To search and view data use the link in the Data and resources section below.

Compilation of GPS data gathered by City staff and volunteers, utilizing handheld GPS units with a level of accuracy <5 meters. This file does not include all protected wildlife located within the City, as a street by street survey has not been completed since 2009. This file is updated frequently as new species locations are reported to the City. The data is current on the date of download only. Although the data points were valid at the time they were collected, use of this data is not guaranteed to include all protected wildlife locations within the City. The intended use of this data is a guide. Ground truthing must be utilized to guarantee protected species are not located on a property and are not impacted by land clearing or construction activities.

CDFW divides the state into six administrative Regions. CDFW staff in each Region identified linear segments of infrastructure that currently present barriers to wildlife populations in their jurisdiction. In doing so, the Regions used all available empirical information in their possession, including existing connectivity and road crossing studies, collared-animal movement data, roadkill observations, and professional expertise. This dataset represents all barriers identified statewide as of May 2022 and former barriers that have been remediated since 2020. This dataset represents CDFW's ongoing effort to identify priority wildlife movement barriers across the state. Currently, increasing attention is being directed toward wildlife habitat connectivity as a mechanism of maintaining biodiversity in the face of population growth and climate change. Listing priority wildlife barrier locations will help focus limited financial resources where the highest need has been identified to improve wildlife movement. This is complementary to CDFW’s fish passage barrier priorities that have been identified for anadromous fish. Like the fish passage priorities, the wildlife barrier priorities list will be periodically updated to reflect new information and barrier removal successes. Most of the barriers identified are highway segments, but other infrastructure types such as fencing, canals, local roads, and high speed rail alignments are also represented. Additional information can be found at https://wildlife.ca.gov/Conservation/Wildlife/Connectivity/Barriers.

Africa Wildlife

## Overview

Africa Wildlife is a dataset for object detection tasks - it contains Animals F62Q annotations for 1,463 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset features over 5,500,000 high-quality images of animals sourced from photographers around the globe. Created to support AI and machine learning applications, it offers a richly diverse and precisely annotated collection of wildlife, domestic, and exotic animal imagery.

Key Features: 1. Comprehensive Metadata: the dataset includes full EXIF data such as aperture, ISO, shutter speed, and focal length. Each image is pre-annotated with species information, behavior tags, and scene metadata, making it ideal for image classification, detection, and animal behavior modeling. Popularity metrics based on platform engagement are also included.

1. Unique Sourcing Capabilities: the images are gathered through a proprietary gamified platform that hosts competitions on animal photography. This approach ensures a stream of fresh, high-quality content. On-demand custom datasets can be delivered within 72 hours for specific species, habitats, or behavioral contexts.

2. Global Diversity: photographers from over 100 countries contribute to the dataset, capturing animals in a variety of ecosystems—forests, savannas, oceans, mountains, farms, and homes. It includes pets, wildlife, livestock, birds, marine life, and insects across a wide spectrum of climates and regions.

3. High-Quality Imagery: the dataset spans from standard to ultra-high-resolution images, suitable for close-up analysis of physical features or environmental interactions. A balance of candid, professional, and artistic photography styles ensures training value for real-world and creative AI tasks.

4. Popularity Scores: each image carries a popularity score from its performance in GuruShots competitions. This can be used to train AI models on visual appeal, species preference, or public interest trends.

5. AI-Ready Design: optimized for use in training models in species classification, object detection, wildlife monitoring, animal facial recognition, and habitat analysis. It integrates seamlessly with major ML frameworks and annotation tools.

6. Licensing & Compliance: all data complies with global data and wildlife imagery licensing regulations. Licenses are clear and flexible for commercial, nonprofit, and academic use.

Use Cases: 1. Training AI for wildlife identification and biodiversity monitoring. 2. Powering pet recognition, breed classification, and animal health AI tools. 3. Supporting AR/VR education tools and natural history simulations. 4. Enhancing environmental conservation and ecological research models.

This dataset offers a rich, high-quality resource for training AI and ML systems in zoology, conservation, agriculture, and consumer tech. Custom dataset requests are welcomed. Contact us to learn more!

This GIS data set represents the Wildlife Management Area system administered by the Florida Fish and Wildlife Conservation Commission (FWC). These data are intended as a general reference map only. More information on activities permitted in individual areas can be found from the links on FWC's Web site: http://www.myfwc.com/RECREATION/WMASites_index.htm

This volume's release consists of 143321 media files captured by autonomous wildlife monitoring devices under the project, Massachusetts Wildlife Monitoring Project. The attached files listed below include several CSV files that provide information about the data release. The file, "media.csv" provides the metadata about the media, such as filename and date/time of capture. The actual media files are housed within folders under the volume's "child items" as compressed files. A critical CSV file is "dictionary.csv", which describes each CSV file, including field names, data types, descriptions, and the relationship of each field to fields in other CSV files. Some of the media files may have been "tagged" or "annotated" by either humans or by machine learning models, identifying wildlife targets within the media. If so, this information is stored in "annotations.csv" and "modeloutputs.csv", respectively. To protect privacy, all personally identifiable information (P ...

Oregon Department of Fish and Wildlife management unit boundaries are published in the Oregon Big Game Hunting Regulations. The mapping was updated in July 2016.

Dataset Card for "big-animal-dataset"

Hi! I combined animals 10 dataset, the oxford pets dataset, stanford dogs dataset, and the cats vs dogs dataset for a large animal dataset. More Information needed

Vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.

Wildlife

## Overview

Wildlife is a dataset for object detection tasks - it contains E Ele annotations for 6,573 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Wildlife Habitats (HAFA) contains data for the 11 legal wildlife habitats located on land under the domain of the State and is protected under the Wildlife Habitat Regulations (RHF). There are also HAFAs located on mixed and private lands for information purposes. Since they are essential environments for wildlife, the eleven habitats benefit from legal protection in Quebec. The conservation of wildlife species and their habitats is beneficial for biodiversity. Each of these species plays an important role in our ecosystems. ### #Mise on guard: The digital version of geo-descriptive data describing wildlife habitats is produced from a legal perspective of location, protection and management of habitats. In fact, only the digital version that has been published in the Official Gazette of Quebec is recognized as legal. Last publication of wildlife habitats: November 17, 2022.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The northern Sierra Nevada foothills (NSNF) wildlife connectivity project modeled wildlife corridors for focal species between 271 landscape blocks within the northern Sierra Nevada foothills and neighboring ecoregions. The linkages incorporate data and information for 30 focal species, including 9 passage species (species that move through the corridor) and 21 corridor dwellers (species that may take more than one generation to move through a corridor). The linkages are made up of a least-cost corridor union and additional habitat patch information for corridor dwellers. The least-cost union is a union of the least-cost corridor analysis, based on species specific habitat models, for nine focal passage species (total number of corridors identified for each species follows the species name): black bear (47), black-tailed jackrabbit (105), bobcat (81), dusky-footed woodrat (98), gray fox (85), mountain lion (66), mule deer (134), western gray squirrel (99) and western pond turtle (84). Many species corridors were overlapping despite diverse habitat needs and the use of species specific data to build the habitat suitability models. Habitat areas for corridor dwellers, based on habitat suitability modeling and patch analysis, was added to the least-cost union: We identified all habitat patches within the corridor union, measured distance between each habitat patch to make sure it was within the maximum dispersal distance for that corridor dweller, and when needed added habitat near the corridor edge to meet the species dispersal needs. Redundant corridors were deleted to provide cleaner linkage areas. This analysis identified multiple swaths of habitat that species have the potential to reside in or move through. To ensure that ecological processes were protected in each linkage, we imposed a minimum width of 1 km for linkages. For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].