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Special Issue "Deep Learning on the Landsat Archive"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 January 2024 | Viewed by 68

Special Issue Editors

Department of Environmental Resources Engineering, Intelligent Geocomputing Laboratory, State University of New York College of Environmental Science and Forestry, Syracuse, NY, USA
Interests: environmental monitoring; machine learning; image fusion; accuracy assessment; interdisciplinary land modeling
Special Issues, Collections and Topics in MDPI journals
Earth Resources Observation and Science (EROS) Center, U.S. Geological Survey, Sioux Falls, SD 57198, USA
Interests: remote sensing; machine learning; computer vision; spatial analysis; uncertainty quantification
Earth Resources Observation and Science (EROS) Center, U.S. Geological Survey, Sioux Falls, SD 57198, USA
Interests: remote sensing; machine learning; artificial intelligence; geoinformatics

Special Issue Information

Dear Colleagues,

Landsat observations offer global, consistent coverage spanning more than fifty years. They have been instrumental in a wide range of studies in land cover and land use, allowing for, in addition, the assessment of natural hazards, urbanization, biodiversity, and climate, among other areas. Landsat’s standing as the workhorse of environmental remote sensing reflects its significant impact. There exists a vast amount of data, freely available and consistently processed, which is ready to offer more discoveries. The application of deep learning (DL) has emerged as a compelling methodology for use to analyze and extract knowledge from this large data archive.  DL has found fruitful ground in a wide range of data-intensive knowledge discovery tasks, e.g., in medical, automotive and security applications.

The goal of this Special Issue is to collect studies that integrate DL methods with the lengthy Landsat observational record. Submissions must use DL methods and be applied on Landsat observations. Non-DL methodologies and non-Landsat observations are also welcome in the context of comparison and calibration/validation, respectively, but not as standalone studies.

We invite manuscripts in:

  • both pixel and patch-based analysis, for example supervised/semi-supervised/unsupervised classification, segmentation, scene labeling and object detection,
  • fusion with other sensors of different spectral and spatial resolutions and/or signal types (e.g., optical, radar, lidar) and super-resolution tasks,
  • the effect and support of reference data types and availability (e.g., sampling strategies, transfer learning),
  • image preprocessing methods, such as sensor calibration/validation and atmospheric correction,
  • time series analysis (e.g., monitoring, forecasting, anomaly detection) and change detection,
  • reviewing collections of validation data appropriate for integration with the Landsat archive.

Prof. Dr. Giorgos Mountrakis
Dr. Pete Doucette
Dr. Neal J. Pastick
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • Landsat classification
  • deep learning
  • time series analysis
  • image fusion
  • image preprocessing
  • classification sampling
  • image segmentation

Published Papers

This special issue is now open for submission.
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