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).

Wildlife

## Overview

Wildlife is a dataset for object detection tasks - it contains Animals annotations for 1,479 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.

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 Detection YoloV5

## Overview

Wildlife Detection YoloV5 is a dataset for object detection tasks - it contains Anlimals annotations for 9,900 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).

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.

Dataset

This dataset was created by Eya ayouka

Contents

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.

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 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

These boundaries are simplified from the U.S. Fish and Wildlife Service Real Estate Interest data layer containing polygons representing tracts of land (parcels) in which the Service has a real estate interest. Interior boundaries between parcels were dissolved to produce a single set of simplified external boundaries for each feature. These are resource grade mapping representations of the U.S. Fish and Wildlife Service boundaries. For legal descriptions of the land represented here, contact the USFWS Realty Office. This map layer was compiled by the U.S. Fish and Wildlife Service. Although these boundaries represent lands administered by the U.S. Fish and Wildlife Service, not all areas are open to the public. Some fragile habitats need to be protected from human traffic and some management areas are closed. The public is urged to contact specific Refuges or other conservation areas before visiting.

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. The dataset represents the ten highest priority barriers identified in each region. This dataset represents CDFWs initial 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 the list also includes a local road, a high speed rail segment, and a concrete water conveyance canal.Additional information can be found in this report: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=178511.Wildlife Movement Barriers - CDFW [ds2867] represents a comprehensive dataset of all barriers identified to date, including those which have been remediated since 2020.

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).**

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

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.

WDFW cartography staff create map content designed to inform map viewers where certain types of recreation opportunities are promoted on WDFW Wildlife Areas. This layer is created from WDFW parcel data using parcel attributes to define where these targeted recreation opportunities exist. There are currently two focused map content areas, one is to support the GoHunt application where hunting opportunities are promoted. The other is used to identify WDFW lands where a Washington Discover Pass is required. The Recreation Access Code, managed in the WDFW_Lands feature class, is used to define which parcels are dissolved into this feature class. Recreation Access Code values that are brought across as a result of a standard definition query are: 1 - Parcels managed within a designated Wildlife Area and not restricted in any way for being displayed on GoHunt or Discover Pass maps; 4 - Parcels designated by the Wildlife Program for exclusion from GoHunt activities; 5 - Parcels designated by the Wildlife Program for exclusion from the Discover Pass. Users of this feature class can use ArcMap definition queries to appropriately display either GoHunt or Discover Pass map content. This feature class displays the finest scale of the Wildlife Area administrative hierarchy that consists of Widlife Area Complexes, Wildlife Areas and Wildlife Area Units. There are several fields in this data that can be used to label maps with the Wildlife Area Unit name.

To satisfy requirements of Article 42 Executive Law and implementing regulations found at 19 NYCRR 602 which describe the identification, assessment and designations of Significant Coastal Fish and Wildlife Habitats under the State's Coastal Management Program. The areas involved are all limited to the perimeter waters of NY State including: Lake Erie, Niagara River, Lake Ontario, St. Lawrence River, Hudson River (to the Troy Dam), and marine waters around NYC and Long Island. Original data were hand-drawn over DOT topographic quadrangle sections and filed with local governments. Each identified habitat has an accompanying text narrative describing the living resources values leading to designation as a Significant Coastal Fish and Wildlife; web links to these narratives are included in the attribute table of this dataset. For information on the SCFWH documentation contact Stephanie Wojtowicz at the Department of State at the address noted below. Digital data set (version 1.1) was previewed by NYS DEC Habitat Inventory Unit and subsequently corrected as per their recommendations. The original digital version of the SCFWHs, Version 1.2, has been replaced with this current version 2.0. Version 2.0 includes changes/updates to the Peconic estuary (2002), north shore of Long Island (2005), the south shore of Long Island (2008), and the Hudson River (2012). Scale: 1:24000View Dataset on the Gateway

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).

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 the ten highest priority barriers identified in each Region and the twelve top priority barriers statewide. This dataset represents CDFWs 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 the list also includes a local road and two high speed rail alignments.Additional information can be found in this report: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=204648. Wildlife Movement Barriers - CDFW [ds2867] represents a comprehensive dataset of all barriers identified to date, including those which have been remediated since 2020.

Remote cameras (“trail cameras”) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector), to consider the impact of a ML model’s performance on its ability to accelerate human labeling. Six participants tagged trail camera images collected from 12 sites in Vermont and Maine, USA (January-September 2022) using three tagging methods (one with ML bounding box assistance and two without assistance).