GeoAI for Earth Observation Imagery: Fundamentals and Practical Applications - Softcover

Über die Autorinnen und Autoren

Dalton Lunga is a group leader for GeoAI and a senior R&D staff scientist at ORNL. He is also an Associate Editor for Geoscience and Remote Sensing Letters. He is an interdisciplinary scientist with expertise in artificial intelligence, computer vision, high-performance computing and remote sensing. Dalton leads multidisciplinary teams and projects focused on developing novel methods at the intersection of AI, computer vision, and geography toward the built and physical environment mapping using earth observation data. His research is impacting the generation of accurate population estimates and information about urban growth and decline, informing disaster response, identifying at-risk areas to support national security application challenges. Prior to ORNL, Dalton was a Team Lead and Senior Research Scientist at the Council for Scientific and Industrial Research, South Africa where he established and led a Data Science for Decision Impact team. He received his Ph.D in Electrical and Computer Engineering from Purdue University, West Lafayette.

Ronny Hänsch is a scientist at the Microwave and Radar Institute of the German Aerospace Center (DLR) where he leads the Machine Learning Team in the Signal Processing Group of the SAR Technology Department. His research interest is computer vision and machine learning with a focus on remote sensing (in particular SAR processing and analysis). He was chair of the GRSS Image Analysis and Data Fusion (IADF) technical committee 2021-23, and serves as co-chair of the ISPRS working group on Image Orientation and Sensor Fusion, as editor in chief of the Geoscience and Remote Sensing Letters. associate editor the ISPRS Journal of Photogrammetry and Remote Sensing, and organizer of the CVPR Workshop EarthVision (2017-2024) and the IGARSS Tutorial on Machine Learning in Remote Sensing (2017-2024). He has extensive experience in organizing remote sensing community competitions (e.g. SpaceNet and the GRSS Data Fusion Contest).

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GeoAI for Earth Observation Imagery: Fundamentals and Practical Applications comprehensively covers AI and Machine Learning methodologies in processing EO imagery. Traditional image processing methods face challenges in handling the increasing volume of EO imagery, leading to deficiencies and limitations in developing practical EO-driven applications. AI-driven approaches are emerging with promising capabilities to enhance the efficiency, accuracy, and scalability of image processing.

The book chapters discuss essential methodologies, including atmospheric compensation, image enhancement techniques, such as denoising and super-resolution, and advanced analysis methods, such as semantic segmentation and object detection. Cutting-edge approaches to computing, automating, and optimizing image processing tasks are presented. Additionally, emerging trends in GeoAI and their implications on future research are reviewed. The book serves as an essential guide for navigating the complexities of EO data and equips the readers with proven best practices to enhance their analytical capabilities.