Search Results for author:
Found 8 papers, 2 papers with code
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks.
TensorBank: Tensor Lakehouse for Foundation Model Training
Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language.
AB2CD: AI for Building Climate Damage Classification and Detection
To achieve robust and accurate evaluations of building damage detection and classification, we evaluated different deep learning models with residual, squeeze and excitation, and dual path network backbones, as well as ensemble techniques.
Predicting ice flow using machine learning
Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered.
Deep learning for Aerosol Forecasting
Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth.
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).
