no code implementations • 20 Dec 2023 • Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman, Bruce Elmegreen, Karthik Mukkavilli, Johannes Schmude, Levente Klein
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source.
no code implementations • 21 Nov 2023 • Ken C. L. Wong, Levente Klein, Ademir Ferreira da Silva, Hongzhi Wang, Jitendra Singh, Tanveer Syeda-Mahmood
To study the use of CNNs on SOC remote sensing, here we propose the FNO-DenseNet based on the Fourier neural operator (FNO).
no code implementations • 2 Nov 2022 • Arka Daw, Kyongmin Yeo, Anuj Karpatne, Levente Klein
Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change.
no code implementations • 14 Jun 2022 • Conrad M Albrecht, Chenying Liu, Yi Wang, Levente Klein, Xiao Xiang Zhu
We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interaction.
no code implementations • 4 Nov 2020 • Chulin Wang, Eloisa Bentivegna, Wang Zhou, Levente Klein, Bruce Elmegreen
Physics-informed neural networks (NN) are an emerging technique to improve spatial resolution and enforce physical consistency of data from physics models or satellite observations.
no code implementations • 4 Nov 2020 • Wang Zhou, Levente Klein
One of the impacts of climate change is the difficulty of tree regrowth after wildfires over areas that traditionally were covered by certain tree species.