DSAIL-Porini: Annotated camera trap image data of wildlife species from a conservancy in Kenya

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dc.contributor.author Mugambi, Lorna
dc.contributor.author Kabi, Jason N.
dc.contributor.author Kiarie, Gabriel
dc.contributor.author Maina, Ciira wa
dc.date.accessioned 2023-02-09T08:08:55Z
dc.date.available 2023-02-09T08:08:55Z
dc.date.issued 2022-12
dc.identifier.uri https://doi.org/10.1016/j.dib.2022.108863
dc.identifier.uri http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/7889
dc.description.abstract For years, zoologists, ecologists, and researchers at large have been using instruments such as camera traps in acquiring images of wild animals non-intrusively for ecological research. The main reason behind ecological research is to in- crease the understanding of various interactions in ecosystems while providing supporting data and information. Due to climate change and the destruction of animal habitats in recent years, researchers have been conducting studies on diminishing populations of various species of interest and the effectiveness of habitat restoration practices. By collect- ing and examining wild animal image data, inferences such as the health, breeding rate, and population of a particular species can be made. This paper presents an annotated camera trap dataset, DSAIL-Porini 1 , consisting of images of wildlife species captured in a conservancy in Nyeri, Kenya. 6 wildlife species are captured in this dataset: impala, bush- buck, Sykes’ monkey, defassa waterbuck, common warthog, and Burchell’s zebra. This dataset was collected using camera traps based on the Raspberry Pi 2, Raspberry Pi Zero, and OpenMV Cam H7. It provides an example of images collected using relatively low-cost hardware platforms. The image dataset can be used in training and testing object detection and classification machine learning models. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.title DSAIL-Porini: Annotated camera trap image data of wildlife species from a conservancy in Kenya en_US
dc.type Article en_US


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