Disaster management (DM) is one of the leading fields that deal with the humanitarian
aspects of emergencies. The field has attracted researchers because of its ever-increasing …

Deep learning models have brought great breakthroughs in building extraction from high-
resolution optical remote-sensing images. Among recent research, the self-attention module …

Craters are one of the most important morphological features in planetary exploration. To
that extent, detecting, mapping and counting craters is a mainstream process in planetary …

We present an unsupervised deep learning approach for post-disaster building damage
detection that can transfer to different typologies of damage or geographical locations …

The intelligent transportation system, especially autonomous vehicles, has seen a lot of
interest among researchers owing to the tremendous work in modern artificial intelligence …

Building rooftop segmentation using deep learning techniques is a popular yet challenging
area of research in computer vision and remote sensing image processing. While recent …

Deep learning techniques through semantic segmentation networks have been widely used
for natural disaster analysis and response. The underlying base of these implementations …

In this paper, we present a large-scale hurricane Michael dataset for visual perception in
disaster scenarios, and analyze state-of-the-art deep neural network models for semantic …

With the growing demand for geospatial data, challenging aerial images with high spatial,
spectral, and temporal resolution achieve excellent development. Currently, deep …

Segmentation of Earth science imagery is an increasingly common task. Among modern
techniques that use Deep Learning, the UNet architecture has been shown to be a reliable …