Machine Learning for Spatiotemporal Remote Sensing Data (2nd Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 1635
Special Issue Editors
Interests: spatial statistics; machine learning; spatiotemporal data mining; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: coastal remote sensing and GIS; monitoring and assessment; coastal hazards and resilience
Special Issues, Collections and Topics in MDPI journals
Interests: information extraction; uncertainty assessment; image processing and analysis; spatial statistics; classification
Special Issues, Collections and Topics in MDPI journals
Interests: soil science; infrared spectroscopy; big data; remote sensing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
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Keywords
- spatiotemporal modeling
- spatiotemporal dependency
- spatiotemporal prediction
- spatiotemporal fusion
- forecast
- machine learning
- deep learning
- regression
- classification
- remote sensing
- forecast
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Related Special Issue
- Machine Learning for Spatiotemporal Remote Sensing Data in Remote Sensing (13 articles)
