Data underlying PhD thesis: AI in the Sky - Advancing Wildlife Survey Methods in Africa with Deep Learning and Aerial Imagery

DOI:10.4121/838e8d53-e7ba-4306-a62c-6ba7a9428f13.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/838e8d53-e7ba-4306-a62c-6ba7a9428f13

Datacite citation style

Xu, Zeyu (2025): Data underlying PhD thesis: AI in the Sky - Advancing Wildlife Survey Methods in Africa with Deep Learning and Aerial Imagery. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/838e8d53-e7ba-4306-a62c-6ba7a9428f13.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

by Z. Xu orcid logo

This dataset supports the PhD research titled “AI in the Sky: Advancing Wildlife Survey Methods in Africa with Deep Learning and Aerial Imagery”. The research aims to improve wildlife monitoring through deep learning and remote sensing. It focuses on object detection and species counting based on aerial surveys over African wildlife reserves. The dataset includes the Aerial Elephant Dataset (AED) annotations, for which bounding boxes in standard VOC format were created to supplement the original point annotations, and an Antelope Dataset provided by African Parks in South Sudan under a research agreement. These annotations support the training and validation of deep learning models such as YOLO, RT-DETR, CenterNet, U-Net, and D2-Net. Supporting scripts for processing, tiling, annotation handling, quality control, and statistical analysis are included to ensure reproducibility.

History

  • 2025-06-30 first online, published, posted

Publisher

4TU.ResearchData

Format

script/.py annotation/.xml

Organizations

University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Department of Natural Resources

DATA

Files (3)