# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## 4 Discussion  
With the overall accuracy of the change detection in the case studies ranging between 85 to 91%, it is clear that medium-resolution data, such as Sentinel-2, can be used to identify the threats from modern land-use activities. When the proportions of false positives and false negatives are examined, however, the need for continued enhancements to the workflow are apparent. For the Aswan case study, false positives were a problem (user's accuracy of 61%). Perhaps more significantly, false negatives were high for Jufra (producer's accuracy of 68%). EAMENA will continue to develop this methodology in the next phase of its research, including introducing classification of the types of changes, as well as focusing on improved accuracy.  
The causes of the error can be evaluated. The relatively low resolution of Sentinel-2 means some changes cannot be identified; high-resolution data, however, remains prohibitively expensive. The possibility of using PlanetScope imagery instead, applied just to the specific area each user is working in, should be explored. In addition, registration errors of about 10 m found between Sentinel-2 images can limit the accuracy of a per-pixel based approach. Limiting data to a single spacecraft and orbit does not significantly improve the co-registration error to offset the loss of data available for the compositing. If this issue is addressed by ESA, the accuracy of our workflow should be improved.  
Experimenting with the selected threshold value for both case studies (0.2) did not lead to improvements. The user input, in this case, is necessary, but difficult and involves some degree of trial-and-error, especially if ground data is not available [24]. Automation of this value should be pursued, drawing on the considerable literature on this subject from the wider field of computer science (see [31]).  
Different methods for change detection could be tested. There are some disadvantages to using a per-pixel spectral difference change detection approach, such as noise and co-registration problems [26] (p5). Use of a moving window could minimise these problems, including the registration error, although at the expense of significantly slowing down the script, a limitation when the script is run in countries with slow internet, such as Libya. The layer arithmetic approach which we are using could be further improved, for example by analysing texture as an additional unit of change [26] (p6) or the variance across a set of images representing a date range instead of comparing composites. Classification of the change-causing features should also be undertaken. Improved results for object-based over pixel-based methods for detecting archaeological features have been emphasised [7].  
In addition, future analysis should be applied to polygons representing the extent of sites, rather than point data. More detailed data of this nature are increasingly being gathered by EAMENA and tested during our winter 2020 programme of training courses. The case studies described in this paper are arid areas where there is little seasonal variation in vegetation; most vegetation is present as a result of irrigation. To apply the workflow to moister parts of the EAMENA study area, it will need to be modified to account for seasonal/cyclical changes in vegetation, for example by integrating the trends in NDVI (Normalised Difference Vegetation Index) over as long a period as the imagery allows and using it to mask the change layer.