Availability of very high-resolution remote sensing images and advancement of deep
learning methods have shifted the paradigm of image classification from pixel-based and …

This article presents a transformer and convolutional neural network (CNN) hybrid deep
neural network for semantic segmentation of very high resolution (VHR) remote sensing …

Deep learning approaches have shown promising results in remote sensing high spatial
resolution (HSR) land-cover mapping. However, urban and rural scenes can show …

Convolutional and adversarial networks are found in various fields of knowledge and
activities. One such field is building design, a multi-disciplinary and multi-task process …

Training deep learning-based change detection (CD) models heavily relies on large labeled
data sets. However, it is time-consuming and labor-intensive to collect large-scale …

We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover
mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite …

Automatic extraction of building footprints from high-resolution satellite imagery has become
an important and challenging research issue receiving greater attention. Many recent …

Urban vegetation mapping is critical in many applications, ie, preserving biodiversity,
maintaining ecological balance, and minimizing the urban heat island effect. It is still …

Monitoring of land cover and land use is crucial in natural resources management.
Automatic visual mapping can carry enormous economic value for agriculture, forestry, or …

Extracting buildings from very high-resolution satellite images is a challenging yet important
task for applications such as urban monitoring. Multiscale feature learning proves to be a …