University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system

Takara Taniguchi, Yudai Ueda, Atsuya Muramatsu, Kohki Hashimoto, Ryo Yagi +2 more
12/5/2025
cs.CVcs.AI

Official code: jo2lxq/wafl (20 stars, GPL-3.0, last commit 2026-04-10).

Abstract

Many industrial sectors have been using of machine learning at inference mode on edge devices. Future directions show that training on edge devices is promising due to improvements in semiconductor performance. Wireless Ad Hoc Federated Learning (WAFL) has been proposed as a promising approach for collaborative learning with device-to-device communication among edges. In particular, WAFL with Vision Transformer (WAFL-ViT) has been tested on image recognition tasks with the UTokyo Building Recognition Dataset (UTBR). Since WAFL-ViT is a mission-oriented sensor system, it is essential to construct specific datasets by each mission. In our work, we have developed the Chulalongkorn University Building Recognition Dataset (CUBR), which is specialized for Chulalongkorn University as a case study in Thailand. Additionally, our results also demonstrate that training on WAFL scenarios achieves better accuracy than self-training scenarios. Dataset is available in https://github.com/jo2lxq/wafl/.

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Code Implementations(1)

jo2lxq/waflOfficial100%
GPL-3.0

Code Space for Wireless Ad Hoc Federated Learning (WAFL) -- A Fully Autonomous Collaborative Learning with Device-to-Device Communication

203PythonMay 7, 20246 months agoGPL-3.0

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Cite this paper

@article{taniguchi2025university,
  title  = {University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system},
  author = {Takara Taniguchi and Yudai Ueda and Atsuya Muramatsu and Kohki Hashimoto and Ryo Yagi and Hideya Ochiai and Chaodit Aswakul},
  year   = {2025},
  eprint = {2512.05468},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.05468v1}
}

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