Foundation models for weather and climate data understanding: A comprehensive survey
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …
sciences is increasingly adopting data-driven models, powered by progressive …
On the opportunities and challenges of foundation models for geoai (vision paper)
Large pre-trained models, also known as foundation models (FMs), are trained in a task-
agnostic manner on large-scale data and can be adapted to a wide range of downstream …
agnostic manner on large-scale data and can be adapted to a wide range of downstream …
Satclip: Global, general-purpose location embeddings with satellite imagery
Geographic location is essential for modeling tasks in fields ranging from ecology to
epidemiology to the Earth system sciences. However, extracting relevant and meaningful …
epidemiology to the Earth system sciences. However, extracting relevant and meaningful …
Positional encoder graph neural networks for geographic data
Graph neural networks (GNNs) provide a powerful and scalable solution for modeling
continuous spatial data. However, they often rely on Euclidean distances to construct the …
continuous spatial data. However, they often rely on Euclidean distances to construct the …
Mission Critical--Satellite Data is a Distinct Modality in Machine Learning
Satellite data has the potential to inspire a seismic shift for machine learning--one in which
we rethink existing practices designed for traditional data modalities. As machine learning …
we rethink existing practices designed for traditional data modalities. As machine learning …
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
In this white paper, we synthesize key points made during presentations and discussions
from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for …
from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for …
[HTML][HTML] Semantic-Enhanced Graph Convolutional Neural Networks for Multi-Scale Urban Functional-Feature Identification Based on Human Mobility
Precise identification of spatial unit functional features in the city is a pre-condition for urban
planning and policy-making. However, inferring unknown attributes of urban spatial units …
planning and policy-making. However, inferring unknown attributes of urban spatial units …
The Weisfeiler-Lehman distance: Reinterpretation and connection with gnns
In this paper, we present a novel interpretation of the Weisfeiler-Lehman (WL) distance
introduced by\cite {chen2022weisfeilerlehman} using concepts from stochastic processes …
introduced by\cite {chen2022weisfeilerlehman} using concepts from stochastic processes …
Geopointgan: Synthetic spatial data with local label differential privacy
Synthetic data generation is a fundamental task for many data management and data
science applications. Spatial data is of particular interest, and its sensitive nature often leads …
science applications. Spatial data is of particular interest, and its sensitive nature often leads …
Towards probabilistic Weather Forecasting with Conditioned Spatio-Temporal Normalizing Flows
C Winkler - arXiv preprint arXiv:2311.06958, 2023 - arxiv.org
Generative normalizing flows are able to model multimodal spatial distributions, and they
have been shown to model temporal correlations successfully as well. These models …
have been shown to model temporal correlations successfully as well. These models …