Earth observation, aiming at monitoring the state of planet Earth using remote sensing data,
is critical for improving our daily lives and living environment. With a growing number of …

Abstract Three-dimensional (3D) building structures are vital to understanding urban
dynamics. Monocular remote sensing imagery is a cost-effective data source for large-scale …

Abstract Existing Earth Vision datasets are either suitable for semantic segmentation or
object detection. In this work, we introduce the first benchmark dataset for instance …

Foundational mapping remains a challenge in many parts of the world, particularly in
dynamic scenarios such as natural disasters when timely updates are critical. Updating …

Extracting buildings from very high-resolution satellite images is a challenging yet important
task for applications such as urban monitoring. Multiscale feature learning proves to be a …

The increasingly common use of incidental satellite images for stereo reconstruction versus
rigidly tasked binocular or trinocular coincident collection is helping to enable timely global …

We present a dataset for building detection and classification from very high-resolution
satellite imagery with the focus on object-level interpretation of individual buildings. It is …

We propose a machine learning based approach for automatic regularization and
polygonization of building segmentation masks. Taking an image as input, we first predict …

We propose a boundary-aware multi-task deep-learning-based framework for fast 3D
building modeling from a single overhead image. Unlike most existing techniques which rely …

Extracting buildings from remote sensing (RS) images is an important task with a variety of
applications. Considerable attention has focused on achieving new state-of-the-art (SOTA) …