Search Results for author: Björn Lütjens

Found 18 papers, 4 papers with code

GEO-Bench: Toward Foundation Models for Earth Monitoring

1 code implementation NeurIPS 2023 Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan David Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Andrew Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vazquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiao Xiang Zhu

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

no code implementations5 Jan 2023 Elijah Cole, Suzanne Stathatos, Björn Lütjens, Tarun Sharma, Justin Kay, Jason Parham, Benjamin Kellenberger, Sara Beery

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

no code implementations23 Jul 2022 Björn Lütjens, Catherine H. Crawford, Campbell D Watson, Christopher Hill, Dava Newman

Numerical simulations in climate, chemistry, or astrophysics are computationally too expensive for uncertainty quantification or parameter-exploration at high-resolution.

Operator learning Uncertainty Quantification

ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery

no code implementations26 Jan 2022 Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Kenza Amara, Attila Steinegger, Ce Zhang, Xiaoxiang Zhu

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

no code implementations1 Dec 2021 Alexandre Lacoste, Evan David Sherwin, Hannah Kerner, Hamed Alemohammad, Björn Lütjens, Jeremy Irvin, David Dao, Alex Chang, Mehmet Gunturkun, Alexandre Drouin, Pau Rodriguez, David Vazquez

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

no code implementations NeurIPS Workshop DLDE 2021 Björn Lütjens, Catherine H Crawford, Mark Veillette, Dava Newman

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

no code implementations17 Sep 2021 Rupa Kurinchi-Vendhan, Björn Lütjens, Ritwik Gupta, Lucien Werner, Dava Newman

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.

Benchmarking BIG-bench Machine Learning +3

Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery

no code implementations23 Jul 2021 Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Xiaoxiang Zhu, Ce Zhang

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

no code implementations5 May 2021 Björn Lütjens, Catherine H. Crawford, Mark Veillette, Dava Newman

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

2 code implementations11 Apr 2021 Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker, Ernest Pokropek, Willa Potosnak, Suyash Bire, Salomey Osei, Björn Lütjens

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.

Graph Neural Networks for Improved El Niño Forecasting

1 code implementation2 Dec 2020 Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker, Ernest Pokropek, Willa Potosnak, Salomey Osei, Björn Lütjens

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

TrueBranch: Metric Learning-based Verification of Forest Conservation Projects

no code implementations21 Apr 2020 Simona Santamaria, David Dao, Björn Lütjens, Ce Zhang

Recent works propose low-cost and accurate MRV via automatically determining forest carbon from drone imagery, collected by the landowners.

Metric Learning

Machine Learning-based Estimation of Forest Carbon Stocks to increase Transparency of Forest Preservation Efforts

no code implementations17 Dec 2019 Björn Lütjens, Lucas Liebenwein, Katharina Kramer

LiDAR-based solutions, used in US forests, are accurate, but cost-prohibitive, and hardly-accessible in the Amazon rainforest.

BIG-bench Machine Learning

Certified Adversarial Robustness for Deep Reinforcement Learning

no code implementations28 Oct 2019 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 on network robustness.

Adversarial Robustness Collision Avoidance +2

Safe Reinforcement Learning with Model Uncertainty Estimates

no code implementations19 Oct 2018 Björn Lütjens, Michael Everett, Jonathan P. How

The importance of predictions that are robust to this distributional shift is evident for safety-critical applications, such as collision avoidance around pedestrians.

Collision Avoidance reinforcement-learning +2

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