46 citations · 95 across the 9 of their papers we have counts for
10 papers
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
Gyri Reiersen, David Dao, Björn Lütjens +5
Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and resto…
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
Rupa Kurinchi-Vendhan, Björn Lütjens, Ritwik Gupta +2
The transition to green energy grids depends on detailed wind and solar forecasts to optimize the siting and scheduling of renewable energy generation. Operational forecasts from n…
Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery
Gyri Reiersen, David Dao, Björn Lütjens +3
Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation. Measuring how much carbon is…
The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks
Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker +5
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-Southern Oscillation (ENSO). However, current dee…
PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling
Björn Lütjens, Catherine H. Crawford, Mark Veillette +1
Climate models project an uncertainty range of possible warming scenarios from 1.5 to 5 degree Celsius global temperature increase until 2100, according to the CMIP6 model ensemble…
Graph Neural Networks for Improved El Niño Forecasting
Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker +4
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-Southern Oscillation (ENSO). However, current dee…
TrueBranch: Metric Learning-based Verification of Forest Conservation Projects
Simona Santamaria, David Dao, Björn Lütjens +1
International stakeholders increasingly invest in offsetting carbon emissions, for example, via issuing Payments for Ecosystem Services (PES) to forest conservation projects. Issui…
Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts
Björn Lütjens, Lucas Liebenwein, Katharina Kramer
An increasing amount of companies and cities plan to become CO2-neutral, which requires them to invest in renewable energies and carbon emission offsetting solutions. One of the ch…
Certified Adversarial Robustness for Deep Reinforcement Learning
Björn Lütjens, Michael Everett, Jonathan P. How
Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees…
Safe Reinforcement Learning with Model Uncertainty Estimates
Björn Lütjens, Michael Everett, Jonathan P. How
Many current autonomous systems are being designed with a strong reliance on black box predictions from deep neural networks (DNNs). However, DNNs tend to be overconfident in predi…