Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …

Abstract Three-dimensional (3D) image mapping of real-world scenarios has a great
potential to provide the user with a more accurate scene understanding. This will enable …

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 Generative Adversarial Network (GAN) is widely used in many generative
problems, including in spatial information sciences and urban systems. The data generated …

Abstract The Normalized Difference Vegetation Index (NDVI) is the most common index to
measure vegetation in agriculture and create classified prescription maps used for several …

Historically, geoscience has been a prominent domain for applications of computer vision
and pattern recognition. The numerous challenges associated with geoscience-related …

Image classification is one of the main drivers of the rapid developments in deep learning
with convolutional neural networks for computer vision. So is the analogous task of scene …

We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images
from land cover maps and auxiliary raster data using generative adversarial networks …

Generation and manipulation of digital images based on deep learning (DL) are receiving
increasing attention for both benign and malevolent uses. As the importance of satellite …

Due to the reduction of technological costs and the increase of satellite launches, satellite
images are becoming more popular and easier to obtain. Besides serving benevolent …