OPENAIRE OpenAire Datacite Sygma UnpayWall Open Research Europe (ORE) Crossref Microsoft Academic Graph Archivio Istituzionale della Ricerca dell'Università degli Studi di Milano Europe PubMed Central arXiv.org e-Print Archive PubMed Central European Union Open Data Portal https://air.unimi.it/bitstream/2434/878015/4/Brandolini%2bet%2bal_ORE_2021_compressed%20%282%29.pdf https://doi.org/2434/878015 Open Research Europe Open Access 2021-09-06 2021-03-24 2020-01-01 2023-04-14 <ns4:p>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 an interdisciplinary dialogue to facilitate complementary landscape management strategies. With the socio-economic values of the “natural” and “cultural” landscape heritage increasingly recognised worldwide, remote sensing tools are being used more and more to facilitate the recording and management of landscape heritage. The advent of freeware cloud computing services has enabled significant improvements in landscape research allowing the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. This research represents one of the first applications of the Google Earth Engine (GEE)  Python application programming interface (API) in studies of historic landscapes. The complete free and open-source software (FOSS) cloud protocol proposed here consists of a Python code script developed in Google Colab, which could be adapted and replicated in different areas of the world. A multi-temporal approach has been adopted to investigate the potential of Sentinel-2 satellite imagery to detect buried hydrological and anthropogenic features along with spectral index and spectral decomposition analysis. The protocol's effectiveness in identifying palaeo-riverscape features has been tested in the Po Plain (N Italy).</ns4:p> FOS: Computer and information sciences Landscape Archaeology Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 0211 other engineering and technologies Articles 02 engineering and technology 15. Life on land 01 natural sciences Fluvial and Alluvial Archaeology 12. Responsible consumption Multispectral analysis Computer Science - Computers and Society Buried features Multispectral analysis;Sentinel-2;Spectral decomposition;Python;Riverscape;Fluvial and Alluvial Archaeology;Landscape Archaeology;Buried features 13. Climate action Computers and Society (cs.CY) 11. Sustainability Spectral decomposition Sentinel-2 Riverscape Python Research Article 0105 earth and related environmental sciences Filippo Brandolini, Guillem Domingo-Ribas, Andrea Zerboni, Sam Turner, 2021-03-24 journalpaper <ns4:p>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 an interdisciplinary dialogue to facilitate complementary landscape management strategies. With the socio-economic values of the “natural” and “cultural” landscape heritage increasingly recognised worldwide, remote sensing tools are being used more and more to facilitate the recording and management of landscape heritage. The advent of freeware cloud computing services has enabled significant improvements in landscape research allowing the rapid exploration and processing of satellite imagery such as the Landsat and Copernicus Sentinel datasets. This research represents one of the first applications of the Google Earth Engine (GEE)  Python application programming interface (API) in studies of historic landscapes. The complete free and open-source software (FOSS) cloud protocol proposed here consists of a Python code script developed in Google Colab, which could be adapted and replicated in different areas of the world. A multi-temporal approach has been adopted to investigate the potential of Sentinel-2 satellite imagery to detect buried hydrological and anthropogenic features along with spectral index and spectral decomposition analysis. The protocol's effectiveness in identifying palaeo-riverscape features has been tested in the Po Plain (N Italy).</ns4:p> A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features 2434/878015 890561