Long term monitoring of the changes in Impervious Surface Areas in a Greek setting using Machine Learning and Remote Sensing data: the case of Athens Greece

Information on Impervious Surface Areas (ISA) is required in various studies related to the urban environment. The continuous expansion of these surfaces is being noticed in large urban centers as a result of urbanization. The development of automated methodologies for mapping the ISas using remote sensing data has experienced a great growth in recent years.

The aim of the present study is the long-term mapping of ISA changes in Athens, Greece, from 1984 to 2022, exploiting the Landsat archive and contemporary methods of geospatial data processing, such as Machine Learning. The study implementation is also carried out in Google Earth Engine cloud platform and the final results obtained are presented in a WebGIS environment.

The results of the present study can contribute to a better understanding of the urban expansions dynamics and the key drivers linked to the urban sprawl that affect cities such as Athens. Furthermore, they can serve as a reference for further development of applications related to urban environments, using machine learning techniques combined with remote sensing data.

 

KEYWORDS: ISA, urban sprawl, Landsat, GEE, WebGIS, Greece