24 citations · 71 across the 10 of their papers we have counts for
10 papers
Multi-task Learning for Source Attribution and Field Reconstruction for Methane Monitoring
Arka Daw, Kyongmin Yeo, Anuj Karpatne +1
Inferring the source information of greenhouse gases, such as methane, from spatially sparse sensor observations is an essential element in mitigating climate change. While it is w…
Aboveground carbon biomass estimate with Physics-informed deep network
Juan Nathaniel, Levente J. Klein, Campbell D. Watson +2
The global carbon cycle is a key process to understand how our climate is changing. However, monitoring the dynamics is difficult because a high-resolution robust measurement of ke…
Monitoring Urban Forests from Auto-Generated Segmentation Maps
Conrad M Albrecht, Chenying Liu, Yi Wang +2
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 interac…
AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning
Conrad M Albrecht, Fernando Marianno, Levente J Klein
A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is…
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process
Chulin Wang, Kyongmin Yeo, Xiao Jin +3
We present a super-resolution model for an advection-diffusion process with limited information. While most of the super-resolution models assume high-resolution (HR) ground-truth…
Quantification of Carbon Sequestration in Urban Forests
Levente J. Klein, Wang Zhou, Conrad M. Albrecht
Vegetation, trees in particular, sequester carbon by absorbing carbon dioxide from the atmosphere. However, the lack of efficient quantification methods of carbon stored in trees r…