Sabah Mohammed, Kim Tai-hoon, Pedram Ghamisi, and Ruay-Shiung Chang


AI in remote sensing

Trends in using AI and ML in geoscience and remote sensing. IoT: Internet of Things.

Artificial intelligence (AI) plays a growing role in geoscience and remote sensing with thousands-of-terabyte images collected every day from satellites, radars, aerial vehicles, drones, aircrafts, and other sensing devices. Machine learning (ML) and other AI technologies are challenged to use these data sets and predict patterns related to atmospheric, environmental, oceanographic, and other changes to the land and the ecosystem of our planet. In the past, remote sensing proved to be successful with typical applications like estimating the typhoon rainfall over oceans, monitoring reservoir water quality, mapping base-metal deposits, landslide detection, soil moisture distribution, crop-type classification, vegetation change detection, and analyzing geological structures. However, modern ML and AI have shifted the focus of remote sensing from using mathematical and statistical techniques for image processing to integrating sophisticated computational intelligence and embedding higher knowledge.

This special issue focuses on recent progress in developing AI and ML methodologies with a variety of Earth science and remote sensing applications. The modern geoscientist needs the power of AI and ML to conduct explorations and predict Earth changes through the use of large numbers of data sets and expertise in smart and intelligent systems. This special issue received nine submissions, from which we have accepted four.

IEEE Geoscience and Remote Sensing Magazine, 9, 2, 7-128, 2021-06-15.

View paper
IARAI Authors
Dr. Pedram Ghamisi
Earth Observation
Machine Learning, Remote Sensing


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