Conference Agenda

The main goal of the recently kicked-off Dragon-6 STAI4CH project is to demonstrate the significant capabilities of optical and radar EO data to provide essential information for archaeological and cultural heritage mapping and monitoring applications, by building upon first achievements by Dragon-5 research collaborations. In particular, STAI4CH aims to develop novel Artificial Intelligence (AI) and Deep learning (DL) methods to process medium and high resolution EO data to efficiently map waterways and waterscapes of ancient Empires in China, Italy and Iraq, and detect physical transformations and land cover changes due to modern development and urbanisation that might impact the heritage assets.

During its first months of activity, STAI4CH’s team has started to design and develop the heritage asset mapping and change detection approaches, which exploit semantic segmentation Neural Network (NN) based on EO self-supervised learning and change detection NN based on multi-source EO data, respectively. Medium resolution EO datasets acquired by Copernicus Sentinel-1 SAR and Sentinel-2 optical missions, and the Chinese sustainable development science satellite SDGSAT-1, have started to be collected for a transect of the UNESCO World Heritage Site (WHS) of the Grand Canal of China, which has been selected as the testing ground to train and validate the newly developed methodologies. In parallel, preparatory work on EO data interpretation approaches to map heritage features in archaeological landscapes of Southern Iraq has been carried out, with the aim to start to generate a knowledgebase to validate the methods when exported to the waterscapes of the UNESCO WHS of the Ahwar marshes. Pivotal has been the role of the Chinese and European Young Scientists in both the algorithmic development and initial experiments in China, and the archaeological mapping in Iraq.

The first results of the AI/DL-based experiments on the Grand Canal transect will be showcased during this presentation, including a discussion on how the methods performed to detect the heritage features and land cover changes, their limitations and pathway to technical improvement. Future work will focus on the enhancement of the performance of the developed methods, their implementation across the whole site of the Grand Canal, and then the testing of replicability through geographical exporting of the AI/DL-based methods on the Ahwar marshes in Iraq and the waterways of the Tiber river in Italy, part of the Historic Centre of Rome UNESCO WHS. Further algorithmic development will include tailoring of the methods to the processing of high resolution imagery from SkySat optical and ICEYE SAR missions, including fine tuning to the local scale for precise mapping and change detection, and also validation of the results based on medium resolution imagery.