Segmentation is an essential step for remote sensing image processing. This study aims to
advance the application of the Segment Anything Model (SAM), an innovative image …

Deep Learning (DL) has a wide variety of applications in various thematic domains,
including spatial information. Although with limitations, it is also starting to be considered in …

Abstract Forest Change Detection (FCD) is a critical component of natural resource
monitoring and conservation strategies, enabling informed decision-making. Various …

Abstract Collaboration between Light Detection and Ranging (LiDAR) point clouds and
deep learning has been proven to be an effective approach for vegetation mapping. Current …

Line segment extraction from point clouds is critical for analyzing and understanding large-
scale scenes. The main challenge is to generate line segments accurately as well as …

We present a neural radiance field method for urban-scale semantic and building-level
instance segmentation from aerial images by lifting noisy 2D labels to 3D. This is a …

Stripe noise is present in the on-orbit images captured by the Sustainable Development
Goals Satellite-1 (SDGSAT-1) Thermal Infrared Spectrometer (TIS). Removing these stripes …

Most urban applications necessitate building footprints in the form of concise vector graphics
with sharp boundaries rather than pixel-wise raster images. This need contrasts with the …

The convolutional neural networks (CNNs) functioning on geometric learning for the urban
large-scale 3D meshes are indispensable because of their substantial, complex, and …

Deep learning has been extensively utilized in the assessment of building damage after
disasters. However, the field of building damage segmentation faces challenges, such as …