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.

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).

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 API accesses data from QLD Government's WildNet database that has been approved for public release. There are a number of functions that retrieve information for species, taxonomy, species lists and reference codes.

The API returns the information available in the WildNet Application

If you are establishing a long-term connection to the API, please email WildNet@detsi.qld.gov.au to be added to the API users email list. This list will be notified when major changes are made to the API.

WildNet information can also be accessed through:

Biomaps provides a map interface to display the WildNet records approved for publication with other spatial layers (such as cadastre, protected areas, vegetation and biodiversity value mapping). A range of WildNet species list reports based on all WildNet records and other environmental reports can be requested for properties and drawn areas etc.

WetlandMaps provides a map interface to display WildNet records approved for publication with other spatial layers (such as wetland mapping).

The Queensland Globe provides a map interface to display WildNet records approved for publication with other spatial layers and themes.

Other WildNet products are made available via the Queensland Government Open Data Portal.

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.

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!

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 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 ...

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 global wildlife identification software market is experiencing robust growth, driven by increasing demand for efficient and accurate wildlife monitoring and management solutions. Factors such as rising concerns about biodiversity loss, the need for effective conservation strategies, and advancements in image recognition technology are fueling market expansion. The market is segmented by application (personal and commercial) and deployment type (on-premise and cloud-based). The cloud-based segment is witnessing faster adoption due to its scalability, accessibility, and cost-effectiveness. Commercial applications, particularly within governmental agencies and research institutions, are dominating the market share, although the personal use segment is gradually expanding as user-friendly applications become more accessible. Key players in the market are continually innovating to enhance the accuracy, speed, and functionality of their software, incorporating advanced features like AI-powered image analysis and species identification algorithms. Geographic distribution shows strong growth in North America and Europe, driven by early adoption and robust research funding. However, Asia-Pacific is expected to demonstrate significant growth potential in the coming years, fueled by increasing conservation efforts and technological advancements in emerging economies. While data privacy and security concerns pose challenges, the overall market outlook remains positive, with a projected Compound Annual Growth Rate (CAGR) exceeding 15% during the forecast period of 2025-2033. The competitive landscape is characterized by a mix of established players and emerging technology companies. Established companies, with their strong market presence and extensive networks, are focusing on product enhancements and strategic partnerships. Smaller, innovative companies are leveraging advancements in AI and machine learning to offer specialized solutions and gain market share. The market’s growth trajectory is also influenced by government initiatives promoting wildlife conservation and biodiversity monitoring. Increasing funding for research and development in this area further fuels the market's expansion. Future growth hinges on the development of more sophisticated AI-driven identification capabilities, particularly for less studied species, and expanding access to affordable, user-friendly software across diverse regions and user demographics. Strategic partnerships between technology providers and conservation organizations will be crucial in maximizing the impact of wildlife identification software on global conservation efforts.

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).

Surveys are conducted to record the number of waterfowl utilizing the Yolo Bypass Wildlife Area. Data are broken up by species and pond number where individuals are counted.

This data and metadata were submitted by California Department of Fish and Wildlife (CDFW) Staff though the Data Management Plan (DMP) framework with the id: DMP000577. For more information, please visit https://wildlife.ca.gov/Data/Sci-Data.

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].

The SSWP Special Status Terrestrial Wildlife Species California Wildlife Habitat Relationships Study is one of many relicensing documents for the South SWP (SSWP) Hydropower Project Number 2426. The California Department of Water Resources and the Los Angeles Department of Water and Power applied to the Federal Energy Regulatory Commission for a new license of the SSWP Project located in Los Angeles County, California along the West Branch of the State Water Project (SWP). The SWP provides southern California with many benefits, including an affordable water supply, reliable regional clean energy, opportunities to integrate green energy, accessible public recreation opportunities, and environmental benefits.