Automated 3D reconstruction based on satellite images has become a research hotspot at
the interdisciplinary of photogrammetry and computer vision. The 3D results based on …

Abstract Knowledge of building height is critical for understanding the urban development
process. High-resolution optical satellite images can provide fine spatial details within urban …

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 …

Urban land-cover information is essential for resource allocation and sustainable urban
development. Recently, deep learning algorithms have shown promising results in land …

Remote sensing images contain a wealth of spatial information. Efficient scene classification
is a necessary precedent step for further application. Despite the great practical value, the …

Multi-Criteria Decision-Making (MCDM) is widely recognized as an effective approach for
identifying optimal solar energy sites. However, a common challenge with MCDM lies in the …

Modern optical satellite sensors enable high-resolution stereo reconstruction from space.
But the challenging imaging conditions when observing the Earth from space push stereo …

We propose a machine learning based approach for automatic 3D building reconstruction
and vectorization. Taking a single-channel photogrammetric digital surface model (DSM) …

Human settlement extent (HSE) and local climate zone (LCZ) maps are both essential
sources, eg, for sustainable urban development and Urban Heat Island (UHI) studies …

Building height and footprint are two fundamental urban morphological features required by
urban climate modelling. Although some statistical methods have been proposed to estimate …