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 …

The development of semi-supervised learning techniques is essential to enhance the
generalization capacities of machine learning algorithms. Indeed, raw image data are …

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 …

We present a new generic method for shadow-aware multi-view satellite photogrammetry of
Earth Observation scenes. Our proposed method, the Shadow Neural Radiance Field (S …

Semantic segmentation (SS) and height estimation (HE) are two critical tasks in remote
sensing scene understanding that are highly correlated with each other. To address both the …

The use of remote sensing imagery has significantly enhanced the efficiency of building
extraction; however, the precise estimation of building height remains a formidable …

Semantic segmentation and height estimation play fundamental roles in the scene
understanding of remote sensing images with their wide variety of aerial applications …

Lensless imaging allows for designing imaging systems that are free from the constraints of
traditional imaging architectures. As a broadly investigated technique, mask-modulated …

Building height serves as an essential feature of urban morphology that provides valuable
insights into human socio-cultural behaviors and their impact on the environment in an …

Novel view synthesis of remote sensing (RS) scenes is of great significance for scene
visualization, human–computer interaction, and various downstream applications. Despite …