Building detection from very high resolution (VHR) optical remote sensing images, which is
an essential but challenging task in remote sensing, has attracted increased attention in …

Abstract We introduce the Satellite Neural Radiance Field (Sat-NeRF), a new end-to-end
model for learning multi-view satellite photogrammetry in the wild. Sat-NeRF combines …

Land cover classification (LCC) is a process used to categorize the earth's surface into
distinct land types. This classification is vital for environmental conservation, urban planning …

The classification of airborne laser scanning (ALS) point clouds is a critical task of remote
sensing and photogrammetry fields. Although recent deep learning-based methods have …

In this paper, we propose a general deep learning based framework, named Sat-MVSF, to
perform three-dimensional (3D) reconstruction of the Earth's surface from multi-view optical …

The increasing availability of 3-D urban data yields new insights into urban developments
and their implications for population density, energy consumption, and the carbon budget …

Deep learning algorithms, especially convolutional neural networks (CNNs), have recently
emerged as a dominant paradigm for high spatial resolution remote sensing (HRS) image …

Accurate change detection of built-up areas (BAs) fosters a comprehensive understanding of
urban development. The post-classification comparison (PCC) is a widely-used change …

Abstract Airborne Laser Scanning (ALS) point cloud classification is a critical task in remote
sensing and photogrammetry communities, which can be widely utilized in urban …

Estimation of the Digital Surface Model (DSM) and building heights from single-view aerial
imagery is a challenging inherently ill-posed problem that we address in this paper by …