Search Results for author:
Found 18 papers, 4 papers with code
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
GEO-Bench: Toward Foundation Models for Earth Monitoring
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.
Teaching Computer Vision for Ecology
Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites.
Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers
Numerical simulations in climate, chemistry, or astrophysics are computationally too expensive for uncertainty quantification or parameter-exploration at high-resolution.
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising but needs to be of high quality in order to replace the current forest stock protocols for certifications.
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
Spectral PINNs: Fast Uncertainty Propagation with Physics-Informed Neural Networks
We aim to quickly quantify the impact of uncertain parameters onto the solution of a PDE - that is - we want to perform fast uncertainty propagation.
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
We provide a thorough and extensible benchmark of leading deep learning-based super-resolution techniques, including the enhanced super-resolution generative adversarial network (ESRGAN) and an enhanced deep super-resolution (EDSR) network, on wind and solar data.
Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery
This proposal paper describes the first systematic comparison of forest carbon estimation from aerial imagery, satellite imagery, and ground-truth field measurements via deep learning-based algorithms for a tropical reforestation project.
PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling
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.
The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks
In comparison, graph neural networks (GNNs) are capable of modeling large-scale spatial dependencies and are more interpretable due to the explicit modeling of information flow through edge connections.
Physically-Consistent Generative Adversarial Networks for Coastal Flood Visualization
Our work aims to enable more visual communication of large-scale climate impacts via visualizing the output of coastal flood models as satellite imagery.
Graph Neural Networks for Improved El Niño Forecasting
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO).
Multivariate Time Series Forecasting
Spatio-Temporal Forecasting
Physics-informed GANs for Coastal Flood Visualization
As climate change increases the intensity of natural disasters, society needs better tools for adaptation.
TrueBranch: Metric Learning-based Verification of Forest Conservation Projects
Recent works propose low-cost and accurate MRV via automatically determining forest carbon from drone imagery, collected by the landowners.
Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts
LiDAR-based solutions, used in US forests, are accurate, but cost-prohibitive, and hardly-accessible in the Amazon rainforest.
Certified Adversarial Robustness for Deep Reinforcement Learning
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 on network robustness.
Safe Reinforcement Learning with Model Uncertainty Estimates
The importance of predictions that are robust to this distributional shift is evident for safety-critical applications, such as collision avoidance around pedestrians.
