As an important application in remote sensing, landcover classification remains one of the
most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly …

With many platforms and sensors continuously observing the earth surface, the large
amount of remote sensing data presents a big data challenge. While remote sensing data …

Although deep neural networks hold the state-of-the-art in several remote sensing tasks,
their black-box operation hinders the understanding of their decisions, concealing any bias …

Currently, a significant amount of research is focused on detecting Marine Debris and
assessing its spectral behaviour via remote sensing, ultimately aiming at new operational …

The diversity and complementarity of sensors available for Earth Observations (EO) calls for
develo** bespoke self-supervised multimodal learning approaches. However, current …

In recent years, supervised learning has been widely used in various tasks of optical remote
sensing image (RSI) understanding, including RSI classification, pixel-wise segmentation …

Carefully curated and annotated datasets are the foundation of machine learning (ML), with
particularly data-hungry deep neural networks forming the core of what is often called …

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 …

Buildings are essential components of urban areas. While research on the extraction and 3D
reconstruction of buildings is widely conducted, information on the fine-grained roof types of …