10 research outputs found
AB2CD: AI for Building Climate Damage Classification and Detection
We explore the implementation of deep learning techniques for precise
building damage assessment in the context of natural hazards, utilizing remote
sensing data. The xBD dataset, comprising diverse disaster events from across
the globe, serves as the primary focus, facilitating the evaluation of deep
learning models. We tackle the challenges of generalization to novel disasters
and regions while accounting for the influence of low-quality and noisy labels
inherent in natural hazard data. Furthermore, our investigation quantitatively
establishes that the minimum satellite imagery resolution essential for
effective building damage detection is 3 meters and below 1 meter for
classification using symmetric and asymmetric resolution perturbation analyses.
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. Overall, the U-Net Siamese network ensemble with F-1 score of 0.812
performed the best against the xView2 challenge benchmark. Additionally, we
evaluate a Universal model trained on all hazards against a flood expert model
and investigate generalization gaps across events, and out of distribution from
field data in the Ahr Valley. Our research findings showcase the potential and
limitations of advanced AI solutions in enhancing the impact assessment of
climate change-induced extreme weather events, such as floods and hurricanes.
These insights have implications for disaster impact assessment in the face of
escalating climate challenges.Comment: 9 pages, 4 figure
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. In this paper we introduce TensorBank, a petabyte scale tensor
lakehouse capable of streaming tensors from Cloud Object Store (COS) to GPU
memory at wire speed based on complex relational queries. We use Hierarchical
Statistical Indices (HSI) for query acceleration. Our architecture allows to
directly address tensors on block level using HTTP range reads. Once in GPU
memory, data can be transformed using PyTorch transforms. We provide a generic
PyTorch dataset type with a corresponding dataset factory translating
relational queries and requested transformations as an instance. By making use
of the HSI, irrelevant blocks can be skipped without reading them as those
indices contain statistics on their content at different hierarchical
resolution levels. This is an opinionated architecture powered by open
standards and making heavy use of open-source technology. Although, hardened
for production use using geospatial-temporal data, this architecture
generalizes to other use case like computer vision, computational neuroscience,
biological sequence analysis and more
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
Machine learning and deep learning methods have been widely explored in
understanding the chaotic behavior of the atmosphere and furthering weather
forecasting. There has been increasing interest from technology companies,
government institutions, and meteorological agencies in building digital twins
of the Earth. Recent approaches using transformers, physics-informed machine
learning, and graph neural networks have demonstrated state-of-the-art
performance on relatively narrow spatiotemporal scales and specific tasks. With
the recent success of generative artificial intelligence (AI) using pre-trained
transformers for language modeling and vision with prompt engineering and
fine-tuning, we are now moving towards generalizable AI. In particular, we are
witnessing the rise of AI foundation models that can perform competitively on
multiple domain-specific downstream tasks. Despite this progress, we are still
in the nascent stages of a generalizable AI model for global Earth system
models, regional climate models, and mesoscale weather models. Here, we review
current state-of-the-art AI approaches, primarily from transformer and operator
learning literature in the context of meteorology. We provide our perspective
on criteria for success towards a family of foundation models for nowcasting
and forecasting weather and climate predictions. We also discuss how such
models can perform competitively on downstream tasks such as downscaling
(super-resolution), identifying conditions conducive to the occurrence of
wildfires, and predicting consequential meteorological phenomena across various
spatiotemporal scales such as hurricanes and atmospheric rivers. In particular,
we examine current AI methodologies and contend they have matured enough to
design and implement a weather foundation model.Comment: 44 pages, 1 figure, updated Fig.
AB2CD: AI for Building Climate Damage Classification and Detection
We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset, comprising diverse disaster events from across the globe, serves as the primary focus, facilitating the evaluation of deep learning models. We tackle the challenges of generalization to novel disasters and regions while accounting for the influence of low-quality and noisy labels inherent in natural hazard data. Furthermore, our investigation quantitatively establishes that the minimum satellite imagery resolution essential for effective building damage detection is 3 meters and below 1 meter for classification using symmetric and asymmetric resolution perturbation analyses. 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. Overall, the U-Net Siamese network ensemble with F-1 score of 0.812 performed the best against the xView2 challenge benchmark. Additionally, we evaluate a Universal model trained on all hazards against a flood expert model and investigate generalization gaps across events, and out of distribution from field data in the Ahr Valley. Our research findings showcase the potential and limitations of advanced AI solutions in enhancing the impact assessment of climate change-induced extreme weather events, such as floods and hurricanes. These insights have implications for disaster impact assessment in the face of escalating climate challenges
Strategic Foresight to Applications of Artificial Intelligence to Achieve Water-related Sustainable Development Goals
UNU-INWEH Reports
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.ISSN:0360-0300ISSN:1557-734
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.ISSN:0360-0300ISSN:1557-734