Advances in Remote Sensing for Exploring Ancient History

Dear Colleagues,

The study of ancient cultures is fundamental for reconstructing the history of the human past. Toward this end, it is important to analyze the relationship between human attendance, environmental and climatic changes, and socio-cultural evolution using appropriate analysis tools, including Earth observation (EO). The multiscale context and the complexity of the issues to be investigated, along with the heterogeneity and the enormous amount of data (including historical sources and archaeological record), require comprehensive approaches also based on the interrelation of EO-based information, Big Data analysis, and artificial intelligence (AI).

This Special Issue aims to present papers addressing the development of models and approaches based on the use of remote sensing techniques to contribute to answering the questions posed by ancient history.

To extract information useful to improve the knowledge of ancient landscapes, the relationship between humans and the environment over time, particular attention will be paid to the integration of data deriving from different sources (including EO data, historical sources, archaeological and geo-archaeological records, etc.) and to their elaboration and interpretation using spatial analysis and AI techniques.

With reference to the use of EO, the focus will be on the application and development of multi-scale approaches (from spaceborne to geophysics and UAS imagery), with passive and active sensors (including SAR and LiDAR), for the processing of time series of data to identify features, patterns, and predictive indicators of cultural interest.

- Holistic approaches based on the use of historical sources and EO data;

- Remote sensing and geophysics data integration and fusion;

- Machine learning for extracting information of cultural interest from EO data;

- Google Earth Engine applied to EO Big Data to identify cultural patterns of ancient landscapes;

- Automatic procedures vs. visual interpretation;

- Modeling of remote sensing for archaeology, geoarchaeology and paleoevironmental studies;

- Theoretical approaches and EO proxy indicators for archaeology.

Remote sensing datasets: VHR and HR satellite imagery (including multispectral and SAR data); airborne LiDAR; UAS-based passive imagery; geophysical data; historical archive of aerial photographs and declassified satellite images

Ancillary data: historical maps; archaeological and geoarchaeological records; historical documents

Prof. Dr. Nicola Masini
Prof. Dr. Apostolos Sarris
Dr. Rosa Lasaponara
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.