Challenges, tasks, and opportunities in modeling agent-based complex systems
Humanity is facing many grand challenges at unprecedented rates, nearly everywhere, and
at all levels. Yet virtually all these challenges can be traced back to the decision and …
at all levels. Yet virtually all these challenges can be traced back to the decision and …
A novel framework for spatio-temporal prediction of environmental data using deep learning
As the role played by statistical and computational sciences in climate and environmental
modelling and prediction becomes more important, Machine Learning researchers are …
modelling and prediction becomes more important, Machine Learning researchers are …
A review of recent researches and reflections on geospatial artificial intelligence
S Gao - Geomatics and Information Science of Wuhan …, 2020 - ch.whu.edu.cn
The technological progress in the field of artificial intelligence (AI) has brought new
opportunities and challenges to the intelligent development and innovative research in …
opportunities and challenges to the intelligent development and innovative research in …
Incorporating spatial autocorrelation in machine learning models using spatial lag and eigenvector spatial filtering features
Applications of machine-learning-based approaches in the geosciences have witnessed a
substantial increase over the past few years. Here we present an approach that accounts for …
substantial increase over the past few years. Here we present an approach that accounts for …
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 …
STGAN: Spatio-temporal generative adversarial network for traffic data imputation
The traffic data corrupted by noise and missing entries often lead to the poor performance of
Intelligent Transportation Systems (ITS), such as the bad congestion prediction and route …
Intelligent Transportation Systems (ITS), such as the bad congestion prediction and route …
Deep generative models for spatial networks
Spatial networks represent crucial data structures where the nodes and edges are
embedded in a geometric space. Nowadays, spatial network data is becoming increasingly …
embedded in a geometric space. Nowadays, spatial network data is becoming increasingly …
Machine learning in geography–Past, present, and future
A Lavallin, JA Downs - Geography Compass, 2021 - Wiley Online Library
This paper concentrates on the different meanings of machine learning (ML) from its origins
to the present and potential future, focusing on contributions within the discipline of …
to the present and potential future, focusing on contributions within the discipline of …
Exploring 20-year applications of geostatistics in precision agriculture in Brazil: what's next?
In the last decades, geostatistics has been widely used for precision agriculture (PA)
producing quite exciting results. Research on this topic is important for sustainable …
producing quite exciting results. Research on this topic is important for sustainable …
Spate-gan: Improved generative modeling of dynamic spatio-temporal patterns with an autoregressive embedding loss
From ecology to atmospheric sciences, many academic disciplines deal with data
characterized by intricate spatio-temporal complexities, the modeling of which often requires …
characterized by intricate spatio-temporal complexities, the modeling of which often requires …