Deep Learning Methods for Aerial Imagery

Dear Colleagues,

Deep learning methods have grown in popularity and have contributed to dramatic increases in performance in various areas of computer vision and other disciplines. In this Special Issue, we invite authors to contribute papers on deep learning methods for aerial imagery using data from unmanned aerial systems and spaceborne or airborne platforms. Aerial imaging has diverse applications, including but not limited to surveillance, environmental monitoring, smart cities, transportation and urban planning, visual odometry, unmanned aerial system obstacle avoidance, precision agriculture, infrastructure mapping and monitoring, land cover, natural resources, construction, geospatial epidemiology, humanitarian assistance, and disaster relief. Proposed algorithms and methods may consider various sensing modalities—e.g., RGB, panchromatic, thermal, multispectral, hyperspectral, SAR, and LIDAR. We invite authors to submit high-quality manuscripts on computer vision and the image analysis of aerial data contributing novel algorithms, systems, review articles, new datasets, or benchmarking studies.

Prof. Dr. Andreas Savakis
Prof. Dr. Erik Blasch
Prof. Dr. Kannappan Palaniappan
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. Sensors 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 2600 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.