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• W2996322789 abstract "The field of remote sensing has recently witnessed major innovations that have been translated to Earth science applications. Before they can be used, remote sensing data must be corrected for effects originating from the sensors, the platforms on which they are deployed, atmospheric characteristics, and geometrical constraints. When the data are calibrated and geolocated, they can be used either as physical quantities, such as reflectance and temperatures, or as images. The recent development of new sensors has permitted the remote measurement of a large area of the Earth's surface, with direct geological applications. Additionally, advances in machine vision, machine learning and artificial intelligence, combined with an unprecedented increase in computer processing power, have lead to innovative remote sensing data processing techniques that simplify the handling of large amounts of complex data. As a consequence, it is now possible to characterize the geological settings of large areas with precision and even their changes through time. Remote sensing data are now directly integrated into modeling algorithms that describe surface and subsurface processes at different scales. Geological remote sensing currently encompasses multi-temporal, multi-source and multi-scale approaches. The retrieval of big data in disseminated archives, as well as (near) real-time processing are the challenges that remain to be solved. These new applications in geology ensure cost-efficient, safe and rapid surveys and monitoring that not only benefit the research community but society at large." @default.
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• W2996322789 date "2021-01-01" @default.
• W2996322789 modified "2023-09-24" @default.
• W2996322789 title "Geological Remote Sensing" @default.
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• W2996322789 doi "https://doi.org/10.1016/b978-0-12-409548-9.12127-x" @default.
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