Abstract Generative Adversarial Networks (GANs) are a type of deep neural network that
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …

The past decade has witnessed the rapid development of geospatial artificial intelligence
(GeoAI) primarily due to the ground-breaking achievements in deep learning and machine …

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …

We present a new method to create spatial data using a generative adversarial network
(GAN). Our contribution uses coarse and widely available geospatial data to create maps of …

ABSTRACT Neural Style Transfer is a Computer Vision topic intending to transfer the visual
appearance or the style of images to other images. Developments in deep learning nicely …

The semantic segmentation of robotic surgery video and the delineation of robotic
instruments are important for enabling automation. Despite major recent progresses, the …

Satellite image analysis is widely used in many real-time applications, from agriculture to the
military. Due to the wide range of Generative Adversarial Network (GAN) applications in …

Traffic state data are key to the proper operation of intelligent transportation systems (ITS).
However, traffic detectors often receive environmental factors that cause missing values in …

Recently, many researchers tried to generate (generalised) maps using deep learning, and
most of the proposed methods deal with deep neural network architecture choices. Deep …

Machine learning is increasingly used as a computing paradigm in cartographic research. In
this extended editorial, we provide some background of the papers in the CaGIS special …