OpenConstruction: A Systematic Synthesis of Open Visual Datasets for Data-Centric Artificial Intelligence in Construction Monitoring
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OpenConstruction is a curated collection of open-access datasets for construction monitoring and analysis.
It provides a centralized resource for researchers, practitioners, and developers working on AI applications in the construction industry.
AI-driven computer vision is transforming construction monitoring, safety management, and productivity. Yet, robust AI development requires access to diverse and well-documented datasets.
This project addresses that need by:
- Compiling a comprehensive catalog of construction datasets
- Standardizing metadata for easier comparison and selection
- Supporting benchmarking and reproducible research
- Facilitating AI model development across tasks and modalities
Our catalog spans a variety of construction-relevant tasks:
- Object Detection
- Semantic Segmentation
- Action Recognition
- Pose Estimation
- Image Captioning
- SLAM / Visual Localization
- Modalities: RGB, Thermal, Depth, LiDAR/Point Cloud, Synthetic
- Annotations: Bounding boxes, segmentation masks, keypoints, captions
- Geographic Coverage: Multi-country, diverse contexts
We welcome community contributions to grow and improve this catalog. Use the quick templates:
This project is enriched by the support and contributions from the community. Thank you for helping grow OpenConstruction!
For questions, suggestions, or collaboration:
- Open an issue
- Email: rxiong3@kent.edu
We thank all dataset creators and contributors for making their resources publicly available to advance AI in construction.
@misc{xiong2025openconstructionsystematicsynthesisopen,
title={OpenConstruction: A Systematic Synthesis of Open Visual Datasets for Data-Centric Artificial Intelligence in Construction Monitoring},
author={Ruoxin Xiong and Yanyu Wang and Jiannan Cai and Kaijian Liu and Yuansheng Zhu and Pingbo Tang and Nora El-Gohary},
year={2025},
eprint={2508.11482},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.11482},
}