## 1 Introduction  
Recently, along with the rapid pace of urbanization, built-up areas are developing rapidly [1]. During urbanization development, human disturbances, mainly construction activities, intensifying environmental degradation [2], as well as water and soil loss, call for immediate action to strengthen its supervision [3]. Construction projects often come in various types, featuring scattered points, long lines, wide areas, and rapid changes. Traditional manual detection methods based on high-resolution remote sensing images can hardly meet the precise supervision requirements of full coverage and multi-temporal phases [3, 4, 5]. Since high-resolution remote sensing images have rich ground object information and fewer spectral bands, remote sensing image classification methods that are based on pixel spectral feature statistics and are \"object-oriented\" are prone to the \"salt and pepper effect\", \"different objects with same spectrum\" and \"same object with different spectrum\", posing greater difficulties for high-resolution image analysis and processing [6, 7]. How to process high-resolution images rapidly, efficiently and automatically has become a hot research topic and challenge in the field of remote sensing.  
With the development of computer vision technologies and remote sensing image interpretation methods, significant progress has been made in target detection and semantic segmentation based on high-resolution optical remote sensing images [8; 9]. The target detection of buildings, planes, vehicles, ships and other objects has achieved high accuracy, mainly because the semantic definition of these objects is relatively clear, their boundaries are well-defined, and their diversity is relatively limited. The construction projects in high-resolution remote sensing images have no unified semantic definition and their scenes include many man-made and natural ground objects with highly non-structured features and significant differences in image features [10; 11; 12]. At present, the identification of construction projects in high-resolution remote sensing images mainly adopts manual interpretation and drawing, but this presents the problem of a large workload of manual interpretation [13]. The proposed object-oriented identification method that selects the optimal segmentation scale also faces the problem of different identification results under different remote sensing image sources and different surface coverage characteristics [14]. However, the existing construction project target detection methods cannot be directly applied to the detection of complex semantic scenes, and the generalization ability of the methods that directly train the detector cannot meet the requirements of accurate extraction [15]. The disadvantage of the previous work is that the semantics of construction projects are very complex, and the research on detection methods for complex scenes is still in its early stage, making it difficult to cope with the exceptionally complex urban scenes of construction projects.  
To solve these problems, the goal of this study is to propose a target detection method for construction projects in complex semantic scenes, which can automatically and accurately identify construction projects in high-resolution remote sensing images. Compared with the traditional target detection methods, this method can improve the identification results by merging the candidate boxes, training the highly informative detailed ground objects and adjusting the confidence of the construction project candidate box, thus improving the detection results. This method can improve the detection performance for construction projects to a certain extent without significantly raising the complexity of training. Furthermore, it can identify the construction period of the construction project (such as topping out or completion) and the implementation of soil and water conservation measures (such as whether the spoil and slag are covered with dust screens) by the features of detailed ground objects.