# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## Materials and Methods
### Change Detection  
This project has developed an initial version of an automated workflow that uses free satellite data and high-performance computing power to monitor land-use changes around the archaeological sites recorded by EAMENA (Figure 2). This is applied to very recent and ongoing datasets, to alert heritage professionals to urgent threats to sites in their region, such as agricultural expansion and construction happening nearby. Google Earth Engine is used because it supplies regularly updated free satellite imagery and high-performance computing. This is a cloud-based platform freely accessible through the internet, which offers a code editor allowing access to a vast catalogue of open-source satellite image data (including Landsat and Sentinel imagery) [23]. Users' own assets (for example, sites from the EAMENA database, or other imagery, such as PlanetScope) can be uploaded and processed within the platform. The EAMENA workflow (Figure 2) comprises computing change between composite Sentinel-2 (Level 2A, surface-reflectance) satellite images using Google Earth Engine. Including other satellite data could improve the sophistication of future incarnations of our workflow, but at present, using just one sensor limits issues caused by radiometric and resolution differences [24]. We use a bespoke script (written in the JavaScript programming language) which the user can easily modify according to their needs (see Supplementary Material for code, archaeological sites not included). The user uploads site points (for example from the EAMENA database) which they want to monitor. They define date ranges and an area of interest. The date selection relies on a function which takes the user-defined date, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{**Date Range**} & \multirow{2}{*}{**Cost**} & **Temporal Resolution** & **Spatial Resolution (m)** & **Radiometric Resolution (um)** \\ \hline Sentinel-2 (L2A) & Since 2017 & Free & 5 days & 10, 20, 60 & 0.493–1.374 \\ \hline PlanetScope & Since 2016 & Limited free use & Daily & 3.7 & 0.455–0.860 \\ \hline VHR via Google & \multirow{2}{*}{Since c.2003} & \multirow{2}{*}{Free} & **Varies, can be** & **Various between** & \\ Earth and QGIS & & & very limited & c. 0.30–1 m & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Satellite imagery used in this study. a unit of change (an integer) and a unit of time (e.g., year). The unit of change is applied to count from the defined date based on the unit of time. For example, if the user selects the date 01-01-2018, the unit of change as \(-1\) and the unit of time as a year, the function will select the time range 01-01-2017 to 01-01-2018. This will be applied to both the earlier and later dates selected by the user. Given this, the user could request changes between two different years to be compared, or two different months or days (from the same or different years). For Aswan, the periods of the start of October 2018-to the end of December 2018 and the start of October 2019-the end of December 2019 were compared. For Jufra, the March-May period for 2019 and 2020 were compared. The Sentinel-2 imagery is split into two collections according to the date function, one representing the later date the user defined and another representing the earlier date they wanted to compare it to. Each collection is then filtered according to the area of interest the user defined and to limit cloud percentage to less than 10%, based on the imagery metadata. Pixels which pass the requirements will be retained in the collection. A cloud mask is applied using a quality assessment band provided by ESA with the data and takes into account dense cloud (high reflectance in blue) and cirrus (high reflectance in a cloud screening band, low reflectance in the blue band, see Reference [25]). The composites are made by calculating the median values of each pixel (in all bands) in the collections.