AIQTrees: A Drone Imagery Dataset for Tree Segmentation

Joseph Chai, Alex To, Barry O’Sullivan, Hoang D. Nguyen
Reliable and Trustworthy Artificial Intelligence 2025, PMLR 310:11-18, 2025.

Abstract

The reliability of AI models typically depends on the data they are trained with, and accurate interpretations require large amounts of data. The scarcity of publicly available datasets is typically encountered for specific small-scale sustainability projects, making data accessibility a limiting factor for developing AI models for semantic segmentation tasks. In sustainability and forestry applications, the usage of UAVs is common due to their lightweight nature and the ability to provide a huge variety of data. In this paper, we present a new dataset of realistic and high-quality drone images taken around sites in Ireland. The images encompass temporal, spatial, and seasonal dimensions, which could alter the tree appearance or illumination conditions of the images and have to be taken into consideration. We also included a baseline benchmark for the semantic segmentation task along with the dataset. It can be accessed at: https://github.com/ReML-AI/AIQTrees.

Cite this Paper

BibTeX
@InProceedings{pmlr-v310-chai25a, title = {AIQTrees: A Drone Imagery Dataset for Tree Segmentation}, author = {Chai, Joseph and To, Alex and O'Sullivan, Barry and Nguyen, Hoang D.}, booktitle = {Reliable and Trustworthy Artificial Intelligence 2025}, pages = {11--18}, year = {2025}, editor = {Nguyen, Hoang D. and Le, Duc-Trong and Björklund, Johanna and Vu, Xuan-Son}, volume = {310}, series = {Proceedings of Machine Learning Research}, month = {12 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v310/main/assets/chai25a/chai25a.pdf}, url = {https://proceedings.mlr.press/v310/chai25a.html}, abstract = {The reliability of AI models typically depends on the data they are trained with, and accurate interpretations require large amounts of data. The scarcity of publicly available datasets is typically encountered for specific small-scale sustainability projects, making data accessibility a limiting factor for developing AI models for semantic segmentation tasks. In sustainability and forestry applications, the usage of UAVs is common due to their lightweight nature and the ability to provide a huge variety of data. In this paper, we present a new dataset of realistic and high-quality drone images taken around sites in Ireland. The images encompass temporal, spatial, and seasonal dimensions, which could alter the tree appearance or illumination conditions of the images and have to be taken into consideration. We also included a baseline benchmark for the semantic segmentation task along with the dataset. It can be accessed at: https://github.com/ReML-AI/AIQTrees.} }
Endnote
%0 Conference Paper %T AIQTrees: A Drone Imagery Dataset for Tree Segmentation %A Joseph Chai %A Alex To %A Barry O’Sullivan %A Hoang D. Nguyen %B Reliable and Trustworthy Artificial Intelligence 2025 %C Proceedings of Machine Learning Research %D 2025 %E Hoang D. Nguyen %E Duc-Trong Le %E Johanna Björklund %E Xuan-Son Vu %F pmlr-v310-chai25a %I PMLR %P 11--18 %U https://proceedings.mlr.press/v310/chai25a.html %V 310 %X The reliability of AI models typically depends on the data they are trained with, and accurate interpretations require large amounts of data. The scarcity of publicly available datasets is typically encountered for specific small-scale sustainability projects, making data accessibility a limiting factor for developing AI models for semantic segmentation tasks. In sustainability and forestry applications, the usage of UAVs is common due to their lightweight nature and the ability to provide a huge variety of data. In this paper, we present a new dataset of realistic and high-quality drone images taken around sites in Ireland. The images encompass temporal, spatial, and seasonal dimensions, which could alter the tree appearance or illumination conditions of the images and have to be taken into consideration. We also included a baseline benchmark for the semantic segmentation task along with the dataset. It can be accessed at: https://github.com/ReML-AI/AIQTrees.
APA
Chai, J., To, A., O’Sullivan, B. & Nguyen, H.D.. (2025). AIQTrees: A Drone Imagery Dataset for Tree Segmentation. Reliable and Trustworthy Artificial Intelligence 2025, in Proceedings of Machine Learning Research 310:11-18 Available from https://proceedings.mlr.press/v310/chai25a.html.

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