Machine Learning for Spatiotemporal Remote Sensing Data (2nd Edition)

Dear Colleagues,

Monitoring spatiotemporal changes in geospatial features such as land cover, land use, and meteorology is critical for practical applications of remotely sensed data. However, spatiotemporal modeling of remote sensing data is challenging due to massive missing values caused by​ clouds or other issues with the high reflectivity, inconsistency, and heterogeneity of spatiotemporal dependencies among geospatial features. Although traditional machine learning methods can include temporal variables in the model to account for temporal variance, due to the lack or limitation of explicit spatiotemporal dependencies in these methods, it may introduce confounding bias by mixing spatial and temporal covariates, especially for classification by remote sensing data. Modern deep learning offers us new opportunities, including flexible network structures such as 3D CNN, CNN-LSTM, CovLSTM, and CNN-Transformer, for explicit spatiotemporal interdependent modeling and efficient parallel computing for processing massive spatiotemporal data input. Whereas deep learning has been widely applied in spatiotemporal predictions in computer vision, natural language processing, meteorology, etc., due to the particularity and complexity of geospatial features, there are many issues to be explored in its use in the spatiotemporal prediction of remote sensing data.

This Special Issue aims to cover machine learning methods and applications in various fields for spatiotemporal regression and classification of remote sensing data. Topics may cover anything from data structure and processing, spatiotemporal fusion, and spatiotemporal interdependent modeling to mechanisms and prediction interpretation. In particular, deep learning methods and their comparisons with other machine learning methods for spatiotemporal modeling are welcome. Articles may address, but are not limited, to the following topics:

• Spatiotemporal modeling by remote sensing;
• Monitoring of land use or land cover by remote sensing;
• Spatiotemporal inversion of geospatial parameters; 
• Spatiotemporal deep learning in remote sensing;
• Predictions by remote sensing;
• Weather forecast by remote sensing.

Prof. Dr. Lianfa Li
Prof. Dr. Xiaomei Yang
Prof. Dr. Yong Ge
Dr. Nikolaos L. Tsakiridis
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.

• spatiotemporal modeling
• spatiotemporal dependency
• spatiotemporal prediction
• spatiotemporal fusion
• forecast
• machine learning
• deep learning
• regression
• classification
• remote sensing
• forecast

• Machine Learning for Spatiotemporal Remote Sensing Data in Remote Sensing (13 articles)