## 4 Analysis and Discussion
### Future Work  
Accurately extracting information from VHR images has always been a challenge in remote sensing. For this reason, traditional methods are usually based on manual interpretation to ensure an accurate extraction. It may not be troublesome to implement regular monitoring by manually updating changes for individual cultural sites. Especially, interpretation based on machine-learning methods is not as accurate as visual interpretation, and the former usually requires experts' intervention. However, obtaining global-monitoring data is a labor-extensive work. Therefore, a semi-intelligent process is still a good option to meet the urgent demand of acquiring global-monitoring data. The developed workflow involves popular machine-learning methods for an easy use. The CVA and RF methods were adopted for the change detection and classification, respectively. The other state-of-the-art methods can be used instead at each step to improve the algorithm performance. Especially, the deep-learning technique can be used for classification in this workflow. The convolutional neural network (CNN), the most popular method in deep learning, can be applied by feeding a huge amount of sample images into the network to derive a well-trained model. This model has a better generalization than machine-learning methods and can then be used to classify images at various study sites, avoiding sample selection at each study site and each time phase, thus cutting off the only step that requires experts' intervention. Consequently, the developed semi-automatic workflow can turn into an automatic one and be efficiently applied to the global CHS. Additionally, introducing intelligent algorithms allows this workflow to be popularized among non-remote-sensing researchers. The changes without conversion types (e.g., reconstruction or destruction of buildings) cannot be detected by this workflow. One solution is to involve additional features for classification such as geometrical and spatial features. Nevertheless, since the classification was performed on the potentially changed objects only, the recall cannot be further improved (Figure 9). Another alternative is to use some more-advanced methods to identify the changes directly without considering the change types, such as a CNN model. It requires a huge amount of sample images labeled with change/unchanged to train a good model, which is worth trying after we generate the first batch of global-monitoring data and derive a reliable sample source. When applying the interference degree to assess the risks of the global CHS, different types of CHS should have different evaluation criteria. The ancient sites, for example, are usually in a natural environment. The ancient architectural buildings, on the other side, are with a more vulnerable structure (e.g., decayed or insect attacked) in most cases. Figure 10: A comparison of the ability to detect delicate changes using traditional monitoring based on manual interpretation and the proposed workflow. **(a)** The whole patch covered by solar panels is identified as the changed patch (delineated in yellow), while the patch covered by sparsely distributed solar panels is missing (in blue rectangle); **(b)** most solar panels can be solely extracted (delineated in red). Thus, ancient sites may have a higher-tolerant value of interference degree than ancient architectural buildings that are categorized of high risk. Moreover, the status of CHS can have a close link to the local economy. The developing places would have a higher demand of land and lead to a high interference degree; the under-developed places may not have enough funds to maintain CHS, causing the interference mainly contributed by natural factors; developed places have already-established protection systems, and the interference degree is expected to be low. Finally, despite the many advantages of Google-Earth imagery, three multispectral bands (red, green, and blue) greatly limit its ability to accurately classify land-cover classes with similar spectral features.