## 1 Introduction  
With urban growth and modernization, the uprising conflict between human activity and heritage conservation has received considerable attention [1]. Monitoring the preservation status of cultural-heritage sites (CHS) is important to maintain their sustainable development [2, 3, 4]. The United Nations has proposed 17 Sustainable Development Goals (SDGs) in 2015 [5]. Among them, Target 11.4 aims to strengthen efforts to protect and safeguard the world's cultural and natural heritage. This target belonged to Tier III (i.e.,definitions, methodologies, or standards are under development) when initially proposed and was classified as Tier II (i.e., indicators are well defined, but data are not regularly collected at country level) in 2019 [6]. However, the realization of this target still faces the challenge of insufficient data and imperfect methods [7].  
The remote-sensing technique has shown a great advantage in cultural-heritage monitoring and management [8; 9; 10; 11; 12; 13]. Remote-sensing images with a low or medium spatial resolution can provide monitoring data with a wide coverage and long time series, and they have been used widely to monitor changes in cultural landscapes of CHS [14; 15; 16; 17]. However, observation at this scale cannot reach the heritage property level, and therefore some key elements surrounding the CHS cannot be observed. For example, individual buildings and narrow roads cannot be monitored from low- or medium-resolution images. The very high-resolution (VHR) remote-sensing images derived from commercial satellites [18], unmanned aerial vehicles (UAV) [19], and Google Earth [20; 21] have become easily accessible, providing the possibility to observe CHS at a fine scale.  
Currently, visually interpretation of CHS and the related targets from VHR images is still the mainstream [22; 23; 24; 25] in cultural-landscape monitoring, and only a few studies applied machine-learning methods for image interpretation [26; 27]. An important reason is that processing VHR images is rather complicated for non-remote-sensing experts. Target 11.4 has put forward the urgent demand of acquiring global CHS data at the fine monitoring scale. To achieve this, more intelligent methods need to be customized and popularized to monitor CHS from VHR images. Another problem is that only one indicator (i.e., the expenditure per capita spent on the preservation) is related to Target 11.4, which is over-simple and not effective to achieve this target [28]. Other complementary indicators should be developed to quantify the preservation status of CHS.  
Therefore, to customize the method of elaborate monitoring the status of CHS, a semi-automatic workflow, was proposed based on the object-based image analysis (OBIA) to identify land-cover changes at two processing levels from bi-temporal VHR images. Afterwards, an indicator named interference degree was proposed to quantify the changes in cultural landscapes within a certain time period. The interference degree can reflect the impacts of human activities and natural factors on the CHS in some degree. Periodically monitoring cultural landscapes can provide a straightforward indicator to evaluate the authenticity and integrity of the CHS. The developed workflow was applied to three representative types of CHS with distinct cultural landscapes for a pilot test. The results demonstrate that this workflow has the potential to monitor global CHS with different types so as to provide the first-hand global-monitoring data at the fine scale.