In land development projects, effective control of the engineering progress is crucial for managing construction quality and costs. However, the conventional approach to monitoring progress is inadequate for large-scale projects. This paper proposes a technique that utilizes UAV images and machine learning techniques to monitor land development projects. The object detection and image segmentation techniques were used to detect essential construction objects. The detected objects were automatically compared to design drawings for checking the progress of the project. Moreover, to ensure personnel safety during construction, an automated process for identifying locations requiring safety barriers was also designed in the study. The effectiveness of all the proposed techniques was evaluated in a real case study. It is illustrated that this fully automated approach for land development monitoring is efficient and thus can contribute to construction safety, cost reduction, and quality assurance in a land development project.

中文翻译：

---

使用无人机和机器学习自动监控土地开发项目的进度


在土地开发项目中，有效控制工程进度对于管理施工质量和成本至关重要。然而，传统的监控进度的方法对于大型项目来说是不够的。本文提出了一种利用无人机图像和机器学习技术来监控土地开发项目的技术。对象检测和图像分割技术用于检测基本的结构对象。检测到的对象会自动与设计图纸进行比较，以检查项目的进度。此外，为了确保施工过程中的人员安全，该研究还设计了一个自动化流程来识别需要安全屏障的位置。在真实案例研究中评估了所有拟议技术的有效性。结果表明，这种全自动的土地开发监控方法是有效的，因此有助于土地开发项目的施工安全、降低成本和质量保证。