About this Research Topic
Nowadays, Artificial Intelligence (AI) has attained a prestigious position in the fields of earth observation, geosciences, and remote sensing. As an interdisciplinary field, AI provides new visions on research areas such as weather forecasting, land-use and land-cover monitoring, management of natural resources, prediction of natural hazards and observing climate changes. This Research Topic emphasizes emerging AI algorithms specifically developed for earth observation encompassing machine learning, deep learning, or data mining. Through the several technologies based on microwave and optical sensors, we aim to explore the methods and tools for handling complex datasets in a variety of applications such as agriculture, cryosphere, land hydrology, oceanography and climate change studies.
With the availability of computational capability and high-resolution satellite datasets, AI has found extensive applications in the emerging scientific domains of remote sensing and geosciences. To explore the current trends of AI in earth exploration, this collection will present practical examples of AI in various earth observation applications. This Research Topic aims to demonstrate the applications of AI for earth sciences problems through the use and evaluation of existing and new methods, such as feature learning, super-resolution mapping, multi-source data fusion, and advanced data analysis models. More emphasis will be given to practical examples to demonstrate the future challenges involved in earth observation via remote sensing.
This Research Topic emphasizes on the emerging AI algorithms specifically developed for earth observation encompassing machine learning, deep learning, and data mining. Examples of algorithms or applications include feature learning processes, classification, change detection, super-resolution mapping, multi-source data fusion, time series analysis and advanced data analysis models. We particularly welcome contributions from three broad themes.
• Current trends of AI in earth observation applications using remote sensing and geosciences.
• AI-based state-of-the-art methods and models for the extraction of earth surface information
• Emerging applications of AI in forecasting and response to natural disasters.
Topic Editor Vishakha Sood is the founder of Aiotronics Automation pvt.ltd. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
With the availability of computational capability and high-resolution satellite datasets, AI has found extensive applications in the emerging scientific domains of remote sensing and geosciences. To explore the current trends of AI in earth exploration, this collection will present practical examples of AI in various earth observation applications. This Research Topic aims to demonstrate the applications of AI for earth sciences problems through the use and evaluation of existing and new methods, such as feature learning, super-resolution mapping, multi-source data fusion, and advanced data analysis models. More emphasis will be given to practical examples to demonstrate the future challenges involved in earth observation via remote sensing.
This Research Topic emphasizes on the emerging AI algorithms specifically developed for earth observation encompassing machine learning, deep learning, and data mining. Examples of algorithms or applications include feature learning processes, classification, change detection, super-resolution mapping, multi-source data fusion, time series analysis and advanced data analysis models. We particularly welcome contributions from three broad themes.
• Current trends of AI in earth observation applications using remote sensing and geosciences.
• AI-based state-of-the-art methods and models for the extraction of earth surface information
• Emerging applications of AI in forecasting and response to natural disasters.
Topic Editor Vishakha Sood is the founder of Aiotronics Automation pvt.ltd. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Keywords: Artificial Intelligence, Remote Sensing, Geospatial Information System (GIS), Earth Monitoring, Machine Learning, Deep Learning, Classification
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