## 2 Materials and Methods
### Experiment Data
#### 2.2.3 Experimental Setting and Evaluation Indicators  
In this experiment, based on expert experience, the six types of detailed ground objects of bare soil (rock), dust screen, construction roads, movable slab houses, buildings under  
Figure 4: Construction projects in (**a**) and detailed ground objects in (**b**) labeling examples.  
construction and constructed buildings were preliminarily selected. According to the selection and expression methods of detailed ground objects, the amount of information about the six types of detailed ground objects was 18.81, 20.90, 9.93, 44.82, 28.77 and 8.22, respectively.  
It is theoretically possible to take all detailed ground objects into account and train all detailed ground object detectors. However, some detailed ground objects have a limited amount of information, producing minor improvement effects for the detection of the construction project and causing increased complexity. Thus, when determining the best detailed ground objects, it is necessary to select the highly informative ones. In this study, four types of detailed ground objects of movable slab houses, buildings under construction, dust screen, and bare soil (rock) were selected to characterize the construction project, and the detectors for these four detailed ground objects were trained.  
In this experiment, both detectors randomly selected 60% of the images in the data set as the training set, 20% of the images as the verification set, and another 20% of the images as the test set. The relevant parameters of the Faster RCNN network are as follows: the initial learning rate is set at 0.001 and drops to 1/10 of the original every 10 rounds of iteration, and the number of training traversals is set at 50. The pre-trained ResNet101 is used as the network structure, the gradient optimization algorithm adopts SGD, the momentum is set to 0.9, and the attenuation coefficient is 0.0005.  
The performance evaluation indicator of this experiment adopts the F1 score, precision-recall curve (PR curve), and single category average precision (AP). The F1 score, also known as the balance F score, is the reconciled average of precision and recall rate. The AP is the area formed by the PR curve, the x and y axis. The PR curve can evaluate the overall situation of the detection results of the construction project, and the AP can objectively evaluate the average quality of the detection of the construction project to a certain extent. The larger the value, the better the target detection model will be.  
In remote sensing object target detection, in general, \(IoU\) (intersection over union) is used as an indicator to evaluate the correctness of the detection of a candidate box. However, due to the particularity of the construction project, there may exist cases where a single candidate box corresponds to multiple ground truths, and \(IoU>0.5\) is not suitable as the indicator to evaluate the correctness of the candidate boxes. Therefore, based on expert knowledge, a one-to-many approach was adopted, where a single candidate box corresponds to multiple ground truths intersecting with it, that is, satisfying \(\sum_{i}^{N}IoU>0.5\). The equation is:  
\[\sum_{i}^{N}IoU=\sum_{i}^{N}\frac{area(D\cap D_{i})}{area(DUD_{i})}, \tag{6}\]  
where \(D\) denotes the candidate box obtained for the detector, and \(D_{i}\) is the \(i\)th ground truth that intersects with the detector.