# SODA: Site Object Detection dAtaset for Deep Learning in Construction

arXiv: https://arxiv.org/abs/2202.09554 · HTML: https://mlsanity.com/paper/2202.09554/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)

## Built on by (most cited first, 6 of 6)

- [Are Large Pre-trained Vision Language Models Effective Construction Safety Inspectors?](https://mlsanity.com/paper/2508.11011/) (2025)
- [VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations](https://mlsanity.com/paper/2305.17927/) (2023)
- [GSO-YOLO: Global Stability Optimization YOLO for Construction Site Detection](https://mlsanity.com/paper/2407.00906/) (2024)
- [Segmentation Dataset for Reinforced Concrete Construction](https://mlsanity.com/paper/2407.09372/) (2024)
- [Impact of color and mixing proportion of synthetic point clouds on semantic segmentation](https://mlsanity.com/paper/2412.19145/) (2024)
- [Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection](https://mlsanity.com/paper/2507.13221/) (2025)

## Abstract (arXiv)

Computer vision-based deep learning object detection algorithms have been developed sufficiently powerful to support the ability to recognize various objects. Although there are currently general datasets for object detection, there is still a lack of large-scale, open-source dataset for the construction industry, which limits the developments of object detection algorithms as they tend to be data-hungry. Therefore, this paper develops a new large-scale image dataset specifically collected and annotated for the construction site, called Site Object Detection dAtaset (SODA), which contains 15 kinds of object classes categorized by workers, materials, machines, and layout. Firstly, more than 20,000 images were collected from multiple construction sites in different site conditions, weather conditions, and construction phases, which covered different angles and perspectives. After careful screening and processing, 19,846 images including 286,201 objects were then obtained and annotated with labels in accordance with predefined categories. Statistical analysis shows that the developed dataset is advantageous in terms of diversity and volume. Further evaluation with two widely-adopted object detection algorithms based on deep learning (YOLO v3/ YOLO v4) also illustrates the feasibility of the dataset for typical construction scenarios, achieving a maximum mAP of 81.47%. In this manner, this research contributes a large-scale image dataset for the development of deep learning-based object detection methods in the construction industry and sets up a performance benchmark for further evaluation of corresponding algorithms in this area.