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).

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.

Animals Wildlife

## Overview

Animals Wildlife is a dataset for object detection tasks - it contains Animals annotations for 328 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

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

Wildlife viewing, defined as intentionally observing, feeding, or photographing wildlife, or visiting or maintaining natural areas because of wildlife, is one of the most popular outdoor recreation activities in the United States. The 2016 National Survey of Hunting, Fishing, and Wildlife-Associated Recreation reported that there are approximately 86 million wildlife viewers aged 16 or older in the U.S. ‒ more than one-third of the adult population ‒ and participation in wildlife viewing has been increasing since the mid-1990s (USDOI et al. 2016). Consistent with national trends, in 2016, about 35% of Virginia’s population viewed wildlife, amounting to 2.1 million wildlife viewers in the state (Rockville Institute, 2020). A growing body of literature shows that wildlife viewers contribute to habitat and wildlife conservation financially, politically, and through participation in other conservation activities (Cooper et al., 2015; Hvenegaard, 2002; McFarlane & Boxall, 1996). In 2016, Virginia wildlife viewers spent over $3.2 billion for their wildlife viewing activities, both in and out of state, on equipment purchases, membership dues and contributions, and trip-related expenses, including food and lodging, transportation, and access fees for public and private lands (Rockville Institute, 2020). Beyond its direct conservation potential, wildlife viewing is also a means of connecting more people to nature (Kellert et al., 2017).

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 ...

Dataset of Animals listed in IUCN Red List.

Animals included in the dataset as of now -> 1. African Elephant 2. Amur Leopard 3. Artic Fox 4. Chimpanzee 5. Orangutan

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].

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.

Adminstrative districts as used by the Iowa DNR Wildlife Bureau.

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).

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.

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.

In 2012, an invasive plant inventory of priority invasive plant species in priority areas was conducted at San Diego National Wildlife Refuge. Results from this effort will inform the development of invasive plant management objectives, strategies, and serves as a baseline for assessing change in the status of invasive plant distribution or abundance over time.

Urban development can alter resource availability, land use, and community composition, in turn influencing wildlife health. Generalizable relationships between wildlife health and urbanization have yet to be quantified, and could vary across health metrics and animal taxonomy. We present a phylogenetic meta-analysis of 516 records spanning 81 wildlife species from 106 studies comparing the toxicant loads, parasitism, body condition, or stress of urban and non-urban wildlife populations in 30 countries. We find a significantly negative relationship between urbanization and wildlife health, driven by higher toxicant loads and greater parasitism by parasites transmitted through close contact. Invertebrates and amphibians were particularly affected, with higher toxicant loads and physiological stress in urban populations as compared to their non-urban counterparts. We also found strong geographic and taxonomic bias in research effort, highlighting future research needs. Our results suggest urban wildlife experience several health risks with potential threats to conservation.

The dataset comprises images from camera traps deployed across various sites in Victoria, Australia, regions to monitor biodiversity and conservation. The ecological experts manually sort and review the camera trap data based on the species class. The reviewed camera trap data is processed through the mega detector model to collect the bounding box coordinates of the species. The proposed dataset consists of three variants to address data imbalance issues in species classification by grouping species into higher-level categories (e.g., birds and small animals) called terrestrial grouped species data, region-specific species data and feral animal data. Each dataset has cropped animal images, YOLO annotated files and COCO formatted JSON files to train efficient deep learning models. All scripts used for data processing, annotation and validation are publicly available in the GitHub repository: GitHub - sameeruddin/ACTD_scripts.