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: 10.4121/68dbfac3-4d56-440f-b919-cd36c66e8115

Xu, Zeyu (2025): Software underlying PhD thesis: AI in the Sky - Advancing Wildlife Survey Methods in Africa with Deep Learning and Aerial Imagery. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/68dbfac3-4d56-440f-b919-cd36c66e8115.v1

Categories

• Ecological Applications
• Environmental and Natural Resource Evaluation
• Flora, Fauna and Biodiversity
• Environmental Sciences
• Environment

Keywords

Artificial intelligence Deep learning Monitoring Object detection Remote sensing

Licence

MIT

Interoperability

• RO-Crate Metadata

Export as...

RefWorks BibTeX Reference Manager Endnote DataCite NLM DC CFF

by Z. Xu

ZyPro is a deep learning framework designed to support the PhD research project “AI in the Sky: Advancing Wildlife Survey Methods in Africa with Deep Learning and Aerial Imagery.” The research aims to improve wildlife monitoring using semantic segmentation and object detection on remote sensing imagery. ZyPro provides tools for training, testing, and predicting with U-Net-based neural networks, as well as specialized modules for remote sensing image processing, such as large image handling, multi-channel data processing, image clipping, tiling, and data augmentation. This software project includes custom loss functions, flexible training pipelines, and various utilities to facilitate high-quality analysis of remote sensing data.

History

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

Publisher

4TU.ResearchData

Format

py

Organizations

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