The EAMENA Machine Learning Automated Change Detection tool (EAMENA MLACD) is a novel geospatial tool developed by EAMENA researcher Dr Ahmed Mahmoud that allows heritage professionals and researchers to rapidly identify and monitor changes and threats to archaeological sites. A comprehensive technical description of the tool and its outputs is provided in Mahmoud et al. 2024 (https://doi.org/10.1016/j.rsase.2024.101396).
The tool uses the cloud computing service Google Earth Engine (https://earthengine.google.com/). It was developed using JavaScript and machine learning algorithms (i.e. Random Forest) to produce a time-series of Sentinel-2 images classified by land cover for a user-defined location and time-period, and compares them to determine threats and changes in land cover and use at and around a defined dataset of heritage sites. (Figure 1).
Figure 1. EAMENA MLACD Framework.
The user-friendly interface and workflow, which requires only basic knowledge of GIS and remote sensing make it a powerful tool which can be adopted by local authorities and heritage professionals in their own regions.
Figure 2. EAMENA MLACD User Interface.
The land cover classification capabilities of the EAMENA MLACD mean that it could also be adapted for further applications in the Earth Observation and Environmental fields such as time series analysis for deforestation, wildfire and flood impact analysis.
Citation: If you use this repository or any part of its contents in your work, please cite the following article: Mahmoud, A.M.A, Sheldrick, N., & Ahmed, M. (2024). A Novel Machine Learning Automated Change Detection Tool for Monitoring Disturbances and Threats to Archaeological Sites. Remote Sensing Applications: Society and Environment 37. doi: 10.1016/j.rsase.2024.101396.