Remote Sensing Information and Sustainable Urban Ecology

Dear Colleagues,

Today, our cities house more than 50% of the global population and are responsible for 70% of global energy consumption and 80% of global CO₂ emissions. Therefore, cities face major challenges and various environmental, societal, and economic problems that must be addressed in order to keep pace with the increasing sustainability requirements. Remote sensing information plays a significant role in developing advanced analytics solutions for sustainable urban ecology. Since advances in remote sensing technologies, cities have generated a huge number of data that offer richer services for citizens. Currently, a myriad of remote sensing sensors are deployed in cities throughout the world, generating various types of remote sensing information, such as optical data, multispectral data, hyperspectral data, lidar data, SAR data, etc. This Special Issue aims to survey state-of-the-art methods, technologies, and systems in remote sensing information, as well as algorithms and methods for the extraction of information from data by AI, computational, and statistical approaches.

Dr. Peng Wang
Prof. Dr. Bo Huang
Prof. Dr. Liguo Wang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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.

• fusion and resolution enhancement for urban remote sensing data
• land cover mapping and classification for urban remote sensing data
• endmember extraction and unmixing for urban remote sensing data
• dimensionality reduction and band selection for urban remote sensing data
• classification and segmentation for urban remote sensing data
• change detection and time-series analysis for urban remote sensing data
• artificial intelligence for remote sensing information in sustainable urban ecology
• deep learning for remote sensing information in sustainable urban ecology