Towards a Multispectral Airborne Light Field Dataset of Forest Animals

Translated title of the contribution: Towards a Multispectral Airborne Light Field Dataset of Forest Animals

Research output: Contribution to conferencePaperpeer-review


Effective monitoring is crucial for conservation efforts, especially in forests, which cover a significant portion of the Earth's surface and are home to diverse ecosystems. Monitoring terrestrial animals often relies on indirect evidence or localized methods, such as camera traps, which provide limited data. Aerial methods, including drones and satellites, are increasingly used but face challenges in dense forest areas. Despite the existence of multiple public airborne wildlife datasets, the ecosystem forest is not addressed so far. For this reason, this work introduces a novel multispectral airborne dataset of forest animals, including spatial information. Like this, the dataset shall act as the foundation for the development of an automated wildlife detection process in forests using modern technologies such as airborne light-field sampling. The proposed dataset will consist of geo-referenced RGB and thermal video data from multiple drone flights over forests, wild animal gates, but also in animal parks with near-natural structured enclosures. So far, 1.62 TB of data (37.53 h footage) have been recorded between April 2022 and June 2023. The dataset mainly contains videos of species native to Austria such as red deer, chamois, roe deer and wild boar. Both the data recording and the labelling are still ongoing.
Translated title of the contributionTowards a Multispectral Airborne Light Field Dataset of Forest Animals
Original languageGerman (Austria)
Publication statusPublished - 8 Sept 2023
EventCamera traps, AI, and Ecology - University of Jena, Jena, Germany
Duration: 7 Sept 20238 Sept 2023
Conference number: 3


WorkshopCamera traps, AI, and Ecology
Abbreviated titleCamTrap
Internet address


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