Improving the interpretability of geospatial artificial intelligence (GeoAI) models has become
critically important to open the 'black box'of complex AI models, such as deep learning. This …

Although explainable artificial intelligence (XAI) promises considerable progress in
glassboxing deep learning models, there are challenges in applying XAI to geospatial …

Water detection from SAR imagery has significant values, such as the flood monitoring and
environmental protection. Nowadays, significant progress has been achieved in water …

This chapter presents some of the fundamental assumptions and principles that could form
the philosophical foundation of GeoAI and spatial data science. Instead of reviewing the well …

Taken literally, geoAI is the use of Artificial Intelligence methods and techniques in solving
geo-spatial problems. Similar to AI more generally, geoAI has seen an influx of new (big) …

Abstract Deep Neural Networks (DNNs) have garnered significant attention across various
research domains due to their impressive performance, particularly Convolutional Neural …

This study utilizes self-organizing maps (SOMs) to investigate the spatiotemporal diffusion
patterns and clusters of the 2022 COVID-19 Omicron variant in Hong Kong, incorporating …

Attention mechanisms have found extensive application in Deep Neural Networks (DNNs),
with numerous experiments over time showcasing their efficacy in improving the overall …

Cartographic map generalization involves complex rules, and a full automation has still not
been achieved, despite many efforts over the past few decades. Pioneering studies show …

In the article Remember to correct the bias when using deep learning for regression,
Christian Igel and Stefan Oehmcke investigate what happens when we use a deep learning …