BB-GeoGPT: A framework for learning a large language model for geographic information science
Large language models (LLMs) exhibit impressive capabilities across diverse tasks in
natural language processing. Nevertheless, challenges arise such as large model …
natural language processing. Nevertheless, challenges arise such as large model …
Neural Bayes estimators for irregular spatial data using graph neural networks
M Sainsbury-Dale, A Zammit-Mangion… - … of Computational and …, 2024 - Taylor & Francis
Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast
and likelihood-free manner. Although they are appealing to use with spatial models, where …
and likelihood-free manner. Although they are appealing to use with spatial models, where …
Geographic location encoding with spherical harmonics and sinusoidal representation networks
Learning feature representations of geographical space is vital for any machine learning
model that integrates geolocated data, spanning application domains such as remote …
model that integrates geolocated data, spanning application domains such as remote …
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
Land cover maps are a pivotal element in a wide range of Earth Observation (EO)
applications. However, annotating large datasets to develop supervised systems for remote …
applications. However, annotating large datasets to develop supervised systems for remote …
Graph-based Large Scale Probabilistic PV Power Forecasting Insensitive to Space-Time Missing Data
In recent years, power systems integrated with distributed energy resources (DERs) have
been considered to mitigate climate change. However, this makes power systems even …
been considered to mitigate climate change. However, this makes power systems even …
Spatial multi-attention conditional neural processes
LL Bao, JS Zhang, CX Zhang - Neural Networks, 2024 - Elsevier
Spatial prediction tasks are challenging when observed samples are sparse and prediction
samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction …
samples are abundant. Gaussian processes (GPs) are commonly used in spatial prediction …
Beyond Grid Data: Exploring graph neural networks for Earth observation
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning
(DL), with applications typically limited to grid-like data structures. Graph Neural Networks …
(DL), with applications typically limited to grid-like data structures. Graph Neural Networks …
Forecasting West Nile virus with graph neural networks: Harnessing spatial dependence in irregularly sampled geospatial data
Abstract Machine learning methods have seen increased application to geospatial
environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield …
environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield …
Classification of spatial objects with the use of graph neural networks
Classification is one of the most-common machine learning tasks. In the field of GIS, deep-
neural-network-based classification algorithms are mainly used in the field of remote …
neural-network-based classification algorithms are mainly used in the field of remote …