Data Analytics and AI Techniques in Remote Sensing

Dear Colleagues,

Data analytics enables us to discover meaningful patterns, draw conclusions, and support decision making by using various techniques and tools to uncover insights from structured and unstructured data through the process of examining, transforming, and modeling data. Artificial Intelligence, often abbreviated as AI, refers to the simulation of human intelligence using AI systems. These systems often use machine learning and deep learning techniques to support reasoning, problem solving, perception, and decision making. Data analytics and AI can be integrated such that AI-based data analytics can analyze vast datasets more quickly and accurately. AI-driven analytics can also provide better predictive insights when predicting future trends, assessing risks, and detecting anomalies. Furthermore, AI-assisted prescriptive analytics can combine historical data with real-time data to provide actionable insights for decision makers.

Remote sensing systems generate ever-increasing amounts of data, for example, in geospatial intelligence gathering through remote sensors for atmospheric monitoring and observation of the earth’s ecosystems. Therefore, there are many important and novel applications of data analytics and AI techniques in remote sensing.

While this Special Issue welcomes the submission of original contributions in the broader area of advancing remote sensing capabilities using data analytics and AI methods, we are especially interested in publishing novel work in harnessing the power of AI techniques to improve data analytics for remote sensing applications. Topics of interest include, but are not restricted to:

• Novel machine learning methods and algorithms for predictive analytics;
• AI for earth observation applications;
• GIS data analytics;
• Theories and models for big geospatial data analysis;
• Compression analytics for classification and anomaly detection;
• AI-assisted automated decision-making tools and systems;
• Intelligent recommendation methods and tools using AI-powered data analytics;
• Real-time remote sensing data analytics using AI;
• High-performance data analytics tools and systems for remote sensing.

Dr. W. David Pan
Dr. Conrad M. Albrecht
Guest Editors

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.

• data analytics
• artificial intelligence
• machine learning
• remote sensing
• deep learning