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Transfer learning in environmental remote sensing
Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
Toward impact-based monitoring of drought and its cascading hazards
Growth in satellite observations and modelling capabilities has transformed drought
monitoring, offering near-real-time information. However, current monitoring efforts focus on …
monitoring, offering near-real-time information. However, current monitoring efforts focus on …
Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems
Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-
relevant scales is critical to mitigating climate change and ensuring sustainable food …
relevant scales is critical to mitigating climate change and ensuring sustainable food …
Applying knowledge-guided machine learning to slope stability prediction
Slope stability prediction is an important task in geotechnical engineering which can be
achieved through physics-based or data-driven approaches. Physics-based approaches …
achieved through physics-based or data-driven approaches. Physics-based approaches …
Knowledge-guided machine learning: Current trends and future prospects
A Karpatne, X Jia, V Kumar - ar** high spatiotemporal resolution LAI
Leaf area index (LAI) is an important variable for characterizing vegetation structure.
Contemporary satellite-based LAI products with moderate spatial resolution, such as those …
Contemporary satellite-based LAI products with moderate spatial resolution, such as those …
Using knowledge-guided machine learning to assess patterns of areal change in waterbodies across the contiguous united states
Lake and reservoir surface areas are an important proxy for freshwater availability.
Advancements in machine learning (ML) techniques and increased accessibility of remote …
Advancements in machine learning (ML) techniques and increased accessibility of remote …
Enhancing spectroscopy-based fruit quality control: A knowledge-guided machine learning approach to reduce model uncertainty
Spectroscopy-based techniques have made remarkable advancements in their application
to fruit quality control but encounter challenges of high model uncertainty arising from …
to fruit quality control but encounter challenges of high model uncertainty arising from …
Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision
Monitoring and modeling the growth of strawberries at the individual fruit level can open up
new opportunities for yield prediction, fruit grading and supply chain optimization. However …
new opportunities for yield prediction, fruit grading and supply chain optimization. However …