## 3 Results
### Main Outcomes of the Top 20 Highly Cited Articles from Each Thematic Area
#### 3.2.6 Cultural Heritage  
EO within the culture heritage thematic area includes detection, monitoring, mapping, and management of archaeological sites [132; 133; 134; 135; 136; 137; 138; 139; 140], looting detection [141; 142], and other threats to cultural heritage sites [143; 144; 145; 146; 147; 148; 149].  
The studies on detection, monitoring, and management employed various satellite data (Corona, Landsat, Spot, Quickbird, Sentinel-2A, COSMO-SkyMed) and reported that there is a need for further development of machine and deep learning techniques in order to automate feature extraction and pattern detection in very high-resolution (VHR) images [132; 134]. [PERSON] et al. [134] tested the application of deep convolutional neural networks (CNNs) for automated remote sensing detection of qanat systems in the Erbil Plain (Iraq), with the use of Corona imagery. They demonstrated that even with small datasets, deep learning can be successfully applied to automate the detection of qanat shafts. According to [PERSON] et al. [137], the combined use of LiDAR, aerial photographs, historical records, and satellite images will further enhance the investigation of ancient sites. By screening long Sentinel-2 time series for Aleppo and Apamea, [PERSON] and [PERSON] [132] showed that changes in textural properties and surface reflectance can be associated with events relevant for conservation. [PERSON] and [PERSON] [133] also suggested the use of COSMO-SkyMed data for condition assessment of archaeological heritage and landscape disturbance due to their capabilities for very high spatial resolution images and for site revisit of up to one day. [PERSON] et al. [139] applied airborne laser scanning (ALS) remote sensing and photogrammetric techniques, including enhancing local topographic contrast for archaeological interpretation, residual relief modeling, sky view factor, and local dominance, and they were able to accurately map known archaeological features and to detect a wide variety of previously unrecorded potential features. A major issue for the preservation of archaeological areas appears to be urban and agriculture sprawling [132; 135; 139; 143; 144; 145]. With the use of multi-temporal and multi-sensor satellite data (Corona, Landsat, Spot, Quickbird, and Sentinel-2A), [PERSON] et al. [135] showed that urban fabric continuously expanded from 1984 to 2017, posing a severe threat at the Theban temples at west Luxor (Egypt).  
[PERSON] and [PERSON] [142] introduced an automatic method for an archaeological looting feature extraction approach (ALFEA) which is based on looting enhancement using spatial autocorrelation, unsupervised classification, and segmentation. For their analysis, they used images available from Google Earth. Their results from the evaluation procedure showed satisfactory performance from two analyzed test cases with a rate of success higher than 90%. According to the review study by [PERSON] and [PERSON] [141], a variety of satellite data were used for the detection of archaeological looting, such as HR multi-spectral imagery from Sentinel-2, VHR X-band SAR data from COSMO-SkyMed, VHR panchromatic and multi-spectral WorldView-2 imagery, and VHR optical data from GeoEye-1, IKONOS-2, QuickBird-2, and WorldView-3; 89% of the studies on looting detection exploited optical images and the majority of these studies used VHR images (less than 1 m resolution). According to the authors, the use of open high-resolution (HR) data (e.g., Sentinel-2 and Landsat) is still at the early stage. Currently, there is lack of common practices in the domain of looting detection from space, thus there is a need to move toward sharing and harmonization of methodologies.  
Apart from looting and urbanization described above, remote sensing techniques have also been applied in other domains which may threaten cultural heritage sites such as erosion [146; 148], earthquakes [149], flooding [147], and salt weathering [144].