Preserving Cultural Heritage wih EO applications and ML
(Vietnamese version: https://geolink.vn/bao-ton-di-san-van-hoa-the-gioi-voi-cong-cu-quan-trac-trai-dat-va-meachine-learning)
Currently, there are many factors effecting the global cultural heritage and need to be monitored in order to preserve it: natural phenomena, such as subsidence and ground motion, and anthropic ones such as urban sprawl and climate change. Earth Observation (EO) data, in this regard, can help by monitoring the degradation of sites, the level of air pollution in the surrounding areas, the coastal erosion or it can help in the discovery of heritage hidden below the ground’s surface and revealing of lost landscapes.
This technology can also provide benefits in terms of risk assessment through the use of change maps, that track the changes occurring within an area of interest during a certain period, and preventive investigations for the realisation of infrastructures.
Space technology application in archaeology
EO data has the potential to provide enhanced cultural areas monitoring over time leveraging on several technologies and techniques: ground penetrating radars (GPR), global navigation satellite systems (GNSS), unmanned aerial vehicles (UAV), artificial intelligence, machine learning and deep learning. A multi-source integration of space-based data and in situ observations is essential in this regard to obtain high level products.
Copernicus data, with the Sentinel satellites, has facilitated a lot the maintenance of cultural heritage not only during natural disasters or conflicts, which can make sites inaccessible, but also in the daily routine, by the monitoring and mapping of the various sites. In the last few years Copernicus has also provided data in systematic time series, enabling monitoring of sites during different seasons and environmental conditions thus reducing field activities. This use of time series can provide useful information to reduce both the impact that urban activities can have on archaeological remains and structures and the impact that unforeseen findings can have on the realisation of new infrastructures. EO data can therefore support local governments in delivering better solutions for the management of the cultural heritage, resulting in savings from the maintenance activities.
Combining remote sensing and machine learning
In recent years, RS-based archaeological research has incorporated machine learning technology and algorithms to automate the detection of archaeological mounds, characteristic elements of permanent and semi-permanent settlements of past cultures using high resolution images (e.g. see figure 1).
Figure 1: SAR and multispectral mound visibility. (A) Google Earth basemap showing the location of a well-preserved mound (yellow circle) and three main land-cover types in the desert edge; (B) dual Sentinel 1 band [VV,VH] in ascending mode; (C) single Sentinel 1 band [VV] in ascending mode; (D) Sentinel 1 false composite in RGB; (E) Sentinel 2 visible composite (B4-B3-B2); and (F) Sentinel 2 false color composite (B8-B4-B3). @Conesa et al., 2020.
Machine learning-based classification (Random Forest classifier) of multi-sensor, multi-temporal satellite data has expanded the known concentration of Indus settlements in the Cholistan Desert in Pakistan reshaping sites characteristics and detecting a series of previously unclassified mounds (figure 2).
Figure 2: Visibility of RF mounds and legacy data. Google basemaps and RF probability fields showing (A) the vectorized new mound of Bokhariyanwala, closely located to multiple legacy coordinates for the same site, and (B) the vectorized new mound of Changalawa, also reported as multiple locations in legacy data probably due to the partial obliteration of the site by an irrigation canal. @Conesa et al., 2020.
Nevertheless, this use of combined technology is not yet largely exploited due to the large computational resources, technological expertise and amount of high quality satellite data required.
Prospect in the future
The combined use of satellite images and machine learning is opening up a new range of possibilities for the discovery of unknown cultural heritage sites, which are estimated in millions around the world, creating new opportunities for a sector that promises very strong growth in the years to come.
In order to accomplish this transition towards a space enabled archaeology, there is a need to spread the awareness of the potential for the cultural heritage sector of Copernicus data, as well as of a whole new wealth of data provided by new space assets (i.e. return on investment driven, targeted data and services provided by the private space actors), . There is often a wide gap, resulting in lack of collaboration and synergies, between cultural heritage managers and experts in space technologies.
The range of actors and stakeholders involved in cultural heritage preservation, discovery and restoration that might take advantage of space derived information is really wide: it might include archaeological parks management, companies providing virtual tours, archaeological research institutes and specialists, environmental organizations, governmental authorities etc.