Photogrammetric Computer Vision in Remote Sensing
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 (31 January 2024) | Viewed by 512
Special Issue Editors
2. Institute of Geodesy, Graz University of Technology, Steyrergasse 30, 8010 Graz, Austria
Interests: photogrammetry; computer vision; remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
2. JOANNEUM RESEARCH Forschungsgesellschaft mbH, DIGITAL, Remote Sensing and Geoinformation, Steyrergasse 17, 8010 Graz, Austria
Interests: remote sensing; time series analysis; LiDAR data assessment; forest monitoring; geoinformation technologies
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; computer vision; machine learning; high performance computing
2. International AI Future Lab, Technical University of Munich (TUM), Lise-Meitner-Straße 9, 85521 Ottobrunn, Germany
Interests: computer vision; remote sensing; machine learning; (veterinary) medicine
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Many applications in the field of remote sensing can only be solved by incorporating methodologies from the fields of photogrammetry and computer vision. In short, remote sensing can be defined as analyzing images of the Earth and other planets, photogrammetry as the art of measuring from images, and computer vision as gaining semantic understanding from images. Thus, this Special Issue aims to collect papers on photogrammetry and computer vision to yield enhanced and custom-tailored performance for remote sensing applications. In summary, the novel field of research called photogrammetric computer vision in remote sensing that bridges the three interdisciplinary topics of interest should be addressed.
This Special Issue aims to collect papers discussing such advances and breakthroughs in photogrammetry and in computer vision to be applied in remote sensing applications. Submitted manuscripts should mainly focus on novelties introduced through recent approaches that link photogrammetry, computer vision, and remote sensing, for example, with the following topics:
- 3D remote sensing with SAR and optical sensors;
- Discrete 3D representation of the surface of the Earth;
- Digital Twin Earth;
- Digital surface, elevation, and terrain models (DSMs, DEMs, DTMs);
- Forest assessment;
- City modeling;
- Land cover and land use classification;
- Carbon reporting;
- Food systems;
- Farmland monitoring;
- Change monitoring;
- Glacier observation;
- Flood prediction;
- Coastal mapping;
- Determination of subsidence;
- Disaster damage mapping.
Dr. Roland Perko
Dr. Manuela Hirschmugl
Dr. Yilei Shi
Prof. Dr. Peter M. Roth
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
- Photogrammetry
- Computer vision
- Remote sensing
- Machine learning
- Deep learning.
