Special Issue "Data Science, Artificial Intelligence and Remote Sensing"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 6487
Interests: landscape and climate research; land surface modelling; terrestrial remote sensing; synthetic aperture radar (SAR); light detection and ranging (LIDAR); forest monitoring, carbon cycle and climate change
Special Issues, Collections and Topics in MDPI journals
Special Issue in Remote Sensing: Remote Sensing and GIS for Habitat Quality Monitoring
Special Issue in Remote Sensing: Advances in Active Remote Sensing of Forests
Special Issue in Remote Sensing: Forest Biomass and Carbon Observation with Remote Sensing
Special Issue in Remote Sensing: Vegetation Dynamics and Forest Structure Monitoring Based on Multisensor Approaches
Special Issue in Land: Feature Papers for Land–Climate Interactions Section
Special Issue in Remote Sensing: Forest Biomass Change and Carbon Dynamics
Data science, the multidisciplinary field of developing algorithms and approaches for deriving insights from big data, artificial intelligence (AI), the simulation of human intelligence with computers, and remote sensing, the collection of large amounts of data from a distance, have all witnessed rapid advances. This Special Issue invites manuscripts that present new data science or AI approaches for deriving inferences from remote sensing data or apply existing data science or AI methods to challenging problems in the broad area of remote sensing. There are no constraints regarding the field of application. Rather, this Special Issue will present the state-of-the-art in data science and AI for the analysis of remote sensing data across various application domains.
- Balzter, H.; Cole, B.; Thiel, C.; Schmullius, C.; Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests. Remote Sens. 2015, 7, 14876-14898.
- Onojeghuo, A.O.; Blackburn, G.A.; Wang, Q.; Atkinson, P.M.; Kindred, D.; Miao, Y. Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data. J. Remote Sens. 2018, 39,1042-1067.
- Gorban, A.N.; Tyukin, I.Y. Blessing of dimensionality: mathematical foundations of the statistical physics of data. Trans. R. Soc. 2018, 376, doi:10.1098/rsta.2017.0237.
Prof. Heiko Balzter
Prof. Ivan Tyukin
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. 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 2500 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.
- data science
- artificial intelligence
- machine learning
- uncertainty quantification
- remote sensing
- high-dimensional data analysis