Data Science and Machine Learning
Submission deadline: 2023-12-30
Section Collection Editors

Section Collection Information

Dear Colleagues,

With the development of earth observation technologies, the comprehensive observation ability of human beings on the earth has reached an unprecedented level. The spatio-temporal data with different imaging methods, different wavebands, different resolutions, different observation scales and dimensions have become the key carrier of geoscience information acquisition, processing and application, and play an increasingly important role in national security and industrial production. At present, the spatio-temporal data processing and analysis technology is not compatible with the data acquisition capability, and the main challenge is how to extract and interpret information from these big data. The increase of data has not improved the prediction ability of the system, but the intelligent analysis of multi-source heterogeneous spatio-temporal data can help to transform spatio-temporal data into geographic knowledge. The rapid development of artificial intelligence technologies, the emergence of large-scale tag data and the significant improvement of computing performance provide opportunities for intelligent analysis of spatio-temporal big data. Machine learning can well learn and extract the spatio-temporal characteristics of complex data, and help to promote the generation of new mechanisms and new knowledge.

For this, it is important to develop new models and methodologies to address big spatial data challenges. Research articles and reviews in this area of study are welcome.  

We look forward to receiving your contributions.

Prof. Dr. Liqiang Zhang

Section Editors


Spatio-Temporal Data; Machine Learning; Deep Learning; Classfication; Segmentation; Spatial Modeling; Spatial Analysis; Data Fusion; Data Mining

Published Paper