Special Issue "Machine Learning and Remote Sensing for Automatic Map Creation and Update"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Dr. Jonathan Rizzi
Guest Editor
Norwegian Institute of Bioeconomy Research (NIBIO), Norway
Interests: big data; bioeconomy; forestry; agriculture; land monitoring; climate

Special Issue Information

Dear Colleagues,

It is my pleasure to announce a new Special Issue of Applied Sciences dedicated to the application of machine learning methods (including deep learning) and remote sensing for the automatic generation or update of maps.

In recent years, we have experienced an exponential increase in remote sensing datasets derived from different sources (satellites, airplanes, UAVs) at different resolutions (up to few cm) based on different sensors (single bands sensors, hyperspectral cameras, LIDAR, etc.). At the same time, parallel developments in IT allow for the storage of very large datasets (up to petabytes) and their efficient processing (through HPC, distributed computing, use of GPUs). This has allowed for the development and diffusion of many libraries and packages implementing machine learning algorithm in a very efficient way. It has, therefore, become possible to use machine learning (including deep learning methods such as convolutional neural networks) for spatial datasets with the aim of increase the level of automaticity for the creation of new maps or in updating existing maps.

In taking this perspective, this Special Issue aims to contribute to the field by presenting the most relevant advances in this research area. The following are some of the topics proposed for this Special Issue (but not limited to):

  • Land cover mapping and land cover changes;
  • Forest resources mapping (both quantity and quality);
  • Crop/vegetation mapping (both quantity and quality);
  • Natural hazards (e.g., presence of disease, drought);
  • Hydrology;
  • Landscape monitoring;
  • Soil monitoring/geology.

Applications can be related to the use of any type of remote sensing data on in any geographical area (including developing countries).

I look forward to your contribution and to read about your latest research.

Dr. Jonathan Rizzi
Guest Editor

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 papers will be 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. Applied Sciences 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 1800 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.


  • machine learning
  • remote sensing
  • big data
  • deep learning
  • land cover mapping
  • forest mapping
  • agriculture mapping
  • satellite
  • UAV
  • natural hazards

Published Papers

This special issue is now open for submission.
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