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Erschienen in: Environmental Earth Sciences 9/2024

01.05.2024 | Editorial

Utilizing geospatial artificial intelligence for remote sensing applications

verfasst von: Alireza Sharifi, Hadi Mahdipour

Erschienen in: Environmental Earth Sciences | Ausgabe 9/2024

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Excerpt

In recent years, there have been notable advancements in geospatial artificial intelligence (Geo AI), which combines spatial analysis and AI, with a main emphasis on integrating spatial principles and ideas into deep learning models (Liu and Song 2024). Geo AI is a rapidly evolving field that combines remote sensing technology with advanced machine learning algorithms to extract meaningful insights from large and complex geospatial datasets. Remote sensing applications rely on the acquisition and analysis of data from airborne or spaceborne sensors, which capture information about the Earth’s surface and atmosphere. Geo AI techniques can be used to enhance the accuracy, efficiency, and scalability of remote sensing data processing, analysis, and interpretation, enabling a wide range of applications in areas such as agriculture, forestry, environmental monitoring, urban planning, and disaster management. Recent trends in Geo AI for remote sensing applications include the development of deep learning models that can process and analyze high-resolution satellite images, the integration of multispectral and hyperspectral data with machine learning algorithms to improve land cover classification and vegetation monitoring, and the use of synthetic aperture radar (SAR) data for terrain mapping, crop yield prediction, and disaster response. However, the research in this area faces several challenges, such as the limited availability of high-quality training data, the need for robust and interpretable models, and the difficulty of integrating heterogeneous and multi-source data. To overcome these challenges, researchers can explore new data acquisition and augmentation strategies, develop explainable AI techniques, and adopt open and collaborative research practices. …

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Literatur
Zurück zum Zitat Bai M, Zhou Z, Li J, Chen Y, Liu J, Zhao X, Yu D (2024) Deep graph gated recurrent unit network-based spatial–temporal multi-task learning for intelligent information fusion of multiple sites with application in short-term spatial–temporal probabilistic forecast of photovoltaic power. Expert Syst Appl 240:122072. https://doi.org/10.1016/j.eswa.2023.122072CrossRef Bai M, Zhou Z, Li J, Chen Y, Liu J, Zhao X, Yu D (2024) Deep graph gated recurrent unit network-based spatial–temporal multi-task learning for intelligent information fusion of multiple sites with application in short-term spatial–temporal probabilistic forecast of photovoltaic power. Expert Syst Appl 240:122072. https://​doi.​org/​10.​1016/​j.​eswa.​2023.​122072CrossRef
Metadaten
Titel
Utilizing geospatial artificial intelligence for remote sensing applications
verfasst von
Alireza Sharifi
Hadi Mahdipour
Publikationsdatum
01.05.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
Environmental Earth Sciences / Ausgabe 9/2024
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI
https://doi.org/10.1007/s12665-024-11584-4

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