A Google Earth Engine-enabled Python approach to improve identification of anthropogenic palaeo-landscape features

The necessity of sustainable development for landscapes has emerged as an important theme in recent decades. Current methods take a holistic approach to landscape heritage and promote interdisciplinary dialogue facilitate complementary management strategies. With the socio-economic values “natural” “cultural” increasingly recognised worldwide, remote sensing tools are being used more recording heritage. Satellite technologies have enabled significant improvements research. The advent cloud-based platform Google Earth Engine (GEE) allowed rapid exploration processing satellite imagery such Landsat Copernicus Sentinel datasets. In this paper, use Sentinel-2 data identification palaeo-riverscape features been assessed Po Plain, selected because it is characterized by human exploitation since Mid-Holocene. A multi-temporal adopted investigate potential detect buried hydrological anthropogenic along with spectral index decomposition analysis. This research represents one first applications GEE Python application programming interface (API) studies. complete free open-source software (FOSS) cloud protocol proposed here consists code script developed Colab which could be simply adapted replicated different areas world.