Advanced AI Technology in Remote Sensing

Dear Colleagues,

Remote sensing, the process of gathering data about Earth's surface from a distance, has long been pivotal in environmental monitoring, disaster management, and resource mapping. However, the exponential growth in data volume and complexity has necessitated more sophisticated analysis techniques. Advanced AI technology has emerged as a game-changer, revolutionizing remote sensing by automating data analysis and unlocking deeper insights from vast datasets. Through machine learning algorithms and neural networks, AI can swiftly interpret remote sensing imagery, detect patterns, and predict environmental changes with unparalleled accuracy. This synergy between AI and remote sensing not only enhances efficiency but also empowers decision-makers with actionable information for addressing pressing global challenges, from climate change mitigation to sustainable land management.

Remote sensing, the process of gathering data about Earth's surface from a distance, has long been pivotal in environmental monitoring, disaster management, and resource mapping. However, the exponential growth in data volume and complexity has necessitated more sophisticated analysis techniques. Advanced AI technology has emerged as a game-changer, revolutionizing remote sensing by automating data analysis and unlocking deeper insights from vast datasets. Through machine learning algorithms and neural networks, AI can swiftly interpret remote sensing imagery, detect patterns, and predict environmental changes with unparalleled accuracy. This synergy between AI and remote sensing not only enhances efficiency but also empowers decision-makers with actionable information for addressing pressing global challenges, from climate change mitigation to sustainable land management.

The Special Issue is focused on automated feature detection, classification, change detection, semantic change detection, earth observation, and Lidar and laser scanning. Articles may address, but are not limited, to the following topics:

• AI-based feature detection and classification;
• Change detection and monitoring;
• Predictive modeling and environmental forecasting;
• Fusion of multi-source remote sensing data;
• AI for remote sensing data analysis in developing regions;
• Smart cities and urban innovation.

Dr. Jing Zhang
Guest Editor

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.

• semantic segmentation
• change detection
• semantic change detection
• earth observation
• lidar and laser scanning
• remote sensing applications