Machine and Deep Learning for Earth Observation Data Analysis

Dear Colleagues,

Earth Observation and remote sensing technologies currently provide enormous volumes of data capable of supporting evidence-based science and informed policy-making. The heterogeneity, the frequency, and, most importantly, the magnitude of those collections of data call for robust and large-scale data analysis methods.

The aim of this Special Issue is to jointly present the new advancements on the topics of Earth Observation, big data, and automated data-driven modeling and analysis which are represented herein by machine and deep learning techniques. The proposed works should be built around and necessarily incorporate the concept of open science (open and free data, open code and software, shared resources, and so on) for precise reproducibility and present large-scale applications (in terms of data, modeling, and computational resources).

Scientists, scholars, and practitioners are invited to submit their original research papers on the following interrelated and not exclusive topics:

• Remote sensing applications: land use, land cover, agricultural monitoring, emergency response, security, water resources observation and management, soil degradation, climate change, etc.;
• Data: training sets for supervised learning, data fusion, sampling methods, granularity, reliability, and completeness;
• Automated data analysis: large-scale machine learning, statistical learning, deep learning, transfer learning, data mining, feature engineering and high-dimensional spaces, parametrization, estimation, optimization;
• Processing: real-time, incremental, CPU/GPU-based, data pre-processing, computing cluster, cloud and distributed processing;
• Spatiotemporal processing: image filtering, morphological analysis, segmentation, tensor signal processing, time-series analysis, adaptive, local and global modeling.

Dr. Vasileios Syrris
Mr. Sveinung Loekken
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. 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.