Dear Colleagues,

Sustainability is crucial for the future of the planet and its inhabitants. Data-driven, informed decisions about future actions regarding the environment depend critically on accurate and up-to-date spatial information about geographically-distributed phenomena and patterns. Moreover, uncertainties stemming from natural hazards and risks can potentially have severe environmental, societal, and economic impacts, and should be included in any urban and regional planning or infrastructure engineering design.

Modern earth observation science can provide this information with unprecedented accuracy and precision to earth and environmental sciences and engineering. However, the sheer quantity and complexity of the data from modern earth observation sensors, and the pressure for timely information, forces us to move beyond traditional mapping and models, and necessitates the need for using and developing novel machine learning computational methods for such data. The purpose of this Special Issue is to investigate the ways the core information technologies of earth observation science on one hand, and machine learning and spatial data science on the other, can provide high quality spatial information and computational models in order to assist sustainability science tackle the complex problems our planet is facing today and secure a sustainable future. These disciplines, integrated under the umbrella of the new field called Computational Sustainability, offer the possibility of true interdisciplinary work in this area. It also offers new perspectives and insights from the “fourth mode of Science,” computational and data-intensive scientific discovery. Work in this field has just started, and there are unique opportunities for all the above disciplines to contribute.

I would like take this opportunity to encourage you to submit articles and scholarly papers on the relevant topics of this Special Issue.

Dr. Evangelos Roussos
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability 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 2400 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.

• computational sustainability
• earth observation science
• machine learning (including deep learning)
• remote sensing and photogrammetry
• geodesy
• environmental sensor networks
• spatial data science
• land use/land cover mapping
• hazards and sustainability
• global change
• geographical information systems