Robotics & Machine Learning Daily News2024,Issue(Dec.18) :80-80.

Researchers from Remote Sensing Technology Institute Report on Findings in Machi ne Learning (Physics-based Machine Learning Emulator of At-sensor Radiances for Solar-induced Fluorescence Retrieval In the O2-a Absorption Band)

Robotics & Machine Learning Daily News2024,Issue(Dec.18) :80-80.

Researchers from Remote Sensing Technology Institute Report on Findings in Machi ne Learning (Physics-based Machine Learning Emulator of At-sensor Radiances for Solar-induced Fluorescence Retrieval In the O2-a Absorption Band)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on Machine Learning are discussed in a new report. According tonews reporting originating from Ober pfaffenhofen, Germany, by NewsRx correspondents, research stated,“The successfu l operation of airborne and space-based spectrometers in recent years holds the promiseto map solar-induced fluorescence (SIF) accurately across the globe. Mac hine learning (ML) can play animportant role in this effort, but its applicatio n to SIF retrieval methods is in part hindered by the needfor time-consuming ra diative transfer modeling to account for atmospheric effects.”

Key words

Oberpfaffenhofen/Germany/Europe/Cybor gs/Emerging Technologies/Machine Learning/Remote Sensing Technology Institute

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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