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.

African Wildlife

## Overview

African Wildlife is a dataset for object detection tasks - it contains Animals 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 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.

Here are a few use cases for this project:

1. Wildlife monitoring and conservation: Researchers and conservationists can use the "wildlife" computer vision model to monitor animal populations in the Colorado region, track species migration patterns, and evaluate the impact of human activities on various wildlife habitats.

2. Public safety and wildlife management: Local authorities and park services may utilize this model to identify specific animal species in urban or protected areas, allowing them to make informed decisions aimed at reducing human-wildlife conflicts and managing wildlife in a sustainable manner.

3. Biodiversity studies and ecosystem research: Environmental scientists can apply the model to gather data on wildlife biodiversity in different habitats across Colorado, helping them better understand ecosystem dynamics, species interactions, and the overall health and stability of regional ecosystems.

4. Wildlife photography and ecotourism: Wildlife enthusiasts, photographers, and ecotourists can utilize the model as a support tool to enhance their understanding of the animals present in their surroundings, assisting them in spotting and identifying specific species during their outdoor adventures in Colorado's natural landscapes.

5. Educational and citizen science initiatives: The "wildlife" computer vision model can be integrated into educational programs, mobile applications, or citizen science projects aiming to raise awareness about Colorado's native wildlife and foster a greater understanding of regional biodiversity, empowering the general public to contribute to conservation efforts.

This dataset includes wildlife species as defined by Prakas No. 20 on the classification and list of wildlife species, which determined that all wildlife species in the Kingdom of Cambodia are state property and a component of forest resources including mammals, birds, reptiles, amphibians, and other invertebrates as well as their spawning grounds.

The USGS National Wildlife Health Center's (NWHC) EPIZOO database is a long term data set that documents over40 years of information on epizootics (epidemics) in wildlife. EPIZOO tracks die-offs throughout the United States and territories, primarily in migratory birds and endangered species. Data include locations, dates, species involved, history, population numbers, total numbers of sick and dead animals, and diagnostic information. Regular data are available from 1975 to the present; some data are available from earlier years. These data represent the most comprehensive documentation of the geographic occurrence of diseases in free-ranging wildlife in existence today. The data are collected from a reporting network developed at NWHC as well as from collaborators across the North American continent.

Through a 2021 AFWA MultiState Conservation Grant, Virginia Tech and the AFWA Wildlife Viewing and Nature Tourism Working Group conducted national and state level surveys to gather more data on wildlife viewers. This dataset is from the survey conducted in South Carolina. It contains: 1. South Carolina Wildlife Viewer Survey.pdf: a pdf version of the survey instrument 2. South Carolina_WildlifeViewerSurvey.csv: a csv (comma-separated values) file of the dataset 3. South Carolina_WildlifeViewerSurvey.sav: a sav (compatible with SPSS, the Statistical Package for Social Science) file of the dataset 4. WildlifeViewerSurveyData_VariableGuide: a guide to each variable name in the datasets.

The illegal import of wildlife and wildlife products is a growing concern, and the U.S. is one of the world’s leading countries in the consumption and transit of illegal wildlife and their derivatives. Yet, few U.S. studies have analyzed the illegal wildlife trade (IWT) on a national or local scale. Moreover, few studies have examined the trends associated with IWT moving through personal baggage. This work aimed to better understand the magnitude of illegal wildlife importation into U.S. ports of entry by determining trends associated with illegal wildlife products from personal baggage seizures in the Pacific Northwest (PNW). To identify the most influential factors in determining the numbers and types of personal baggage seizures into PNW, we analyzed 1,731 records between 1999 and 2016 from the Fish and Wildlife Service’s (FWS) Law Enforcement Management Information System (LEMIS) database. We found five significant contributors: taxonomic Class of wildlife, categorical import date, wildlife product, source region, and the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) status. While wildlife seizures across taxonomic categories have decreased in the PNW since 2008, other findings provide a reason for concern. Three main findings of this study include: (1) mammals make up the majority of seizures (2) temporal trends of wildlife seizures point to increases in seizures in many taxonomic groupings and (3) the majority of seizures originate from six regions, of which East Asia is the largest source. This work adds to the growing understanding of IWT through large-scale geographical seizure data using a highly important global port as our case study.

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 goal of this project is to create a comprehensive dataset with annotated wildlife footage to support wildlife conservation efforts. This annotated dataset will be invaluable for training machine learning models to monitor and protect endangered species and their habitats.

Through a 2021 AFWA MultiState Conservation Grant, Virginia Tech and the AFWA Wildlife Viewing and Nature Tourism Working Group conducted national and state level surveys to gather more data on wildlife viewers. This dataset is from the survey conducted in Kansas. It contains: 1. Kansas Wildlife Viewer Survey.pdf: a pdf version of the survey instrument 2. Kansas_WildlifeViewerSurvey.csv: a csv (comma-separated values) file of the dataset 3. Kansas_WildlifeViewerSurvey.sav: a sav (compatible with SPSS, the Statistical Package for Social Science) file of the dataset 4. WildlifeViewerSurveyData_VariableGuide: a guide to each variable name in the datasets.

The Species of Greatest Conservation Need National Database is an aggregation of lists from State Wildlife Action Plans. Species of Greatest Conservation Need (SGCN) are wildlife species that need conservation attention as listed in action plans. In this database, we have validated scientific names from original documents against taxonomic authorities to increase consistency among names enabling aggregation and summary. This database does not replace the information contained in the original State Wildlife Action Plans. The database includes SGCN lists from 56 states, territories, and districts, encompassing action plans spanning from 2005 to 2022. State Wildlife Action Plans undergo updates at least once every 10 years by respective wildlife agencies. The SGCN list data from these action plans have been compiled in partnership with individual wildlife management agencies, the United States Fish and Wildlife Service, and the Association of Fish and Wildlife Agencies. The SGCN ...

Wildlife Detect Classify Count

## Overview

Wildlife  Detect Classify Count is a dataset for object detection tasks - it contains Wildlife annotations for 674 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).

Adminstrative districts as used by the Iowa DNR Wildlife Bureau.

To create a dataset for YOLO-based object detection, we compile 1500 images across four classes: buffalo, elephant, rhino, and zebra, preprocessed for optimal model training.

Common Road Animals 2

## Overview

Common Road Animals 2 is a dataset for instance segmentation tasks - it contains Common Road Animals annotations for 510 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).

Incidents responded to by the Baton Rouge Animal Control and Rescue Center (ACRC). ACRC is responsible for carrying out duties related to animal-related situations, including: administering the anti-rabies vaccination, licensing, and tag program; investigating animal cruelty incidents; investigating dog fighting; resolving dangerous animal situations; rescuing injured animals; investigating abandoned animal cases; investigating occult, animal sacrifice, and bestiality cases; resolving stray animal situations; enforcing the leash law and owned animal problems; assisting law enforcement with narcotics, evictions, and DWI cases; enforcing barking dog cases; inspecting dog yards/pens; chaining or tethering compliance; assisting animal welfare groups with feral interventions; and conducting educational programs. As many of the incidents included within this data set involve active cases that are currently under investigation and computerized system limitations do not allow for automated screening of open/closed cases, the identity of animal owners is redacted to protect the privacy of the animal owner. Members of the public interested in the identity of a specific incident may contact ACRC directly to inquire about the incident and, if it is closed, ACRC will release a copy of the file to the person requesting it. However, location data regarding where the incident was reported or occurred is included within this data set, which may or may not be the same location as the animal owner's home or property. In addition, to protect the identity of the complainant (person filing the complaint or alerting ACRC to a potential incident), only the complainant's street name is included as part of this data set. Finally, while all incidents are updated on a daily basis, incidents involving animal cruelty are updated based on a rolling 30-day delay to allow for ACRC to investigate the incident and make a determination as to the validity of the cruelty complaint.

Explore the wonders of the ocean with our Aquatic Animal Image Dataset! This collection features a variety of high-quality images showcasing different species of marine life. Perfect for researchers, students, and AI enthusiasts interested in marine biology or image classification. Dive in and discover the beauty beneath the surface! 🌊🐠 #AquaticLife #ImageClassification #MarineBiology

National Wildlife RefugesThis U.S. Fish and Wildlife Service (FWS) feature layer depicts National Wildlife Refuges. These Refuges display external boundary of lands and waters administered by FWS. According to FWS, "Each unit of the Refuge System — whether it is a wildlife refuge, a marine national monument, a conservation area or a waterfowl production area — is established to serve a statutory purpose that targets the conservation of native species dependent on its lands and water. All activities on those acres are reviewed for compatibility with this statutory purpose."Patuxent Research RefugeData currency: current Federal service (National Wildlife Refuge System Boundaries)Data modification(s): NoneFor more information: National Wildlife Refuge SystemFor feedback please contact: ArcGIScomNationalMaps@esri.comFish and Wildlife Service (FWS)Per FWS, " The U.S. Fish and Wildlife Service is the premier government agency dedicated to the conservation, protection, and enhancement of fish, wildlife and plants, and their habitats. We are the only agency in the federal government whose primary responsibility is the conservation and management of these important natural resources for the American public."