Special issue on the application of remote sensing spatio-temporal big data to effective environmental monitoring and sustainable development.

Sun, Genyun; Ren, Jinchang; Sun, Qian; Jia, Mingming

Abstract

The rapid advancement of remote sensing, data science, and geographic information technology has ushered in an era of substantial information explosion. Massive spatio-temporal big data provides rich data resources and technical means have facilitated new opportunities for large-scale and complex monitoring of land and ocean resources and environment, urban changes and natural disasters[1-2]. Nevertheless, the utilization of spatio-temporal big data still faces challenges. How to integrate these data so as to understand land and ocean changes more comprehensively and systematically requires breakthroughs in some basic theories and key technologies[3-4]. In the context of the rapid development of artificial intelligence, the fusion of remote sensing spatio-temporal big data and big models, geospatial analysis and other methods provides technical support for us to better understand the diversity and changes of the land and marine environment, and provides a theoretical basis for more effective environmental monitoring and resource utilization.

Citation

SUN, G., REN, J., SUN, Q. and JIA, M. 2024. Special issue on the application of remote sensing spatio-temporal big data to effective environmental monitoring and sustainable development. Journal of geodesy and geoinformation science [online], 7(4), pages 2-3. Available from: https://doi.org/10.11947/j.JGGS.2024.0401

• Journal Article Type: Editorial
• Acceptance Date: Dec 25, 2024
• Online Publication Date: Jan 17, 2025
• Publication Date: Dec 25, 2024
• Deposit Date: Jan 30, 2025
• Publicly Available Date: Jan 31, 2025
• Journal: Journal of geodesy and geoinformation science
• Print ISSN: 2096-5990
• Electronic ISSN: 2096-5990
• Publisher: Surveying and Mapping Press
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 4
• Pages: 2-3
• DOI: https://doi.org/10.11947/j.JGGS.2024.0401
• Keywords: Remote sensing; Data science; Geographic information technology; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2675512