Machine Learning Methods for Polar Regions
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 7408
Special Issue Editors
Interests: data mining; machine learning; knowledge discovery using remote sensing data; ontologies and semantic representations; benchmarks for earth observation data
Interests: efficient information extraction from multimodal remote sensing; nonlinear signal processing applied to large-scale heterogeneous records; earth observation interpretation and big data mining; analysis and management for human–environment interaction assessment
Special Issues, Collections and Topics in MDPI journals
Interests: Earth observation; remote sensing; machine learning; deep learning; computer vision; geophysical image processing; statistical modeling; synthetic aperture radar; data science
Special Issue Information
Dear Colleagues,
Polar regions are remote and hostile environments where efforts to collect in situ observations and data are limited by very real constraints, such as the weather, lack of infrastructure, and long periods of polar darkness during winter. Consequently, satellite platforms provide the only source of consistent, repeatable, regional-scale, year-round data of the polar regions, but parameters extracted from them still lack the necessary veracity.
This Special Issue aims to put together contributions from the ExtremeEarth and AI4Arctic projects, but also other similar projects.
We cordially invite researchers who have an interest in this topic to submit their original papers. Potential topics for this Special Issue are related (but not limited only) to the following topics:
- Artificial intelligence/machine learning/deep learning techniques
- Copernicus and third-party data
- Big data
- Fusion data
- Earth Observation training data
- Sea-ice charts
- Linked geospatial data
- Spatiotemporal evolution patterns
- Semantic multisensor satellite image time series analysis
- Knowledge and extreme earth analytics
Dr. Corneliu Octavian Dumitru
Dr. Andrea Marinoni
Mr. Tore Wulf
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 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 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.
Keywords
- Machine learning
- Semantics/ontologies
- Benchmark data
- Linked data
- Sea-ice charts
