Abstract Generative Adversarial Networks (GANs) are one of the most creative advances in
Deep Learning (DL) in recent years. The Remote Sensing (RS) community has adopted …

Deep neural networks have attained great success in handling high dimensional data,
especially images. However, generating naturalistic images containing ginormous subjects …

Image classification plays an important role in remote sensing. Earth observation (EO) has
inevitably arrived in the big data era, but the high requirement on computation power has …

Automatic building extraction from optical imagery remains a challenge due to, for example,
the complexity of building shapes. Semantic segmentation is an efficient approach for this …

Building extraction in VHR RSIs remains a challenging task due to occlusion and boundary
ambiguity problems. Although conventional convolutional neural networks (CNNs) based …

In today's world, satellite images are being utilized for the identification of built-up area,
urban planning, disaster management, insurance & tax assessment in an area, and many …

Building footprint maps are vital to many remote sensing (RS) applications, such as 3-D
building modeling, urban planning, and disaster management. Due to the complexity of …

The urban building stock has a high demand for materials and energy, exerting tremendous
pressure on natural resources. A current research trend is to integrate Building Material …

Accurate building footprint polygons provide essential data for a wide range of urban
applications. While deep learning models have been proposed to extract pixel-based …

Accurate and reliable building footprint maps are vital to urban planning and monitoring, and
most existing approaches fall back on convolutional neural networks (CNNs) for building …