Automated extraction of roads from remotely sensed data come forth various usages ranging
from digital twins for smart cities, intelligent transportation, urban planning, autonomous …

We present Argoverse, a dataset designed to support autonomous vehicle perception tasks
including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a …

We propose a motion forecasting model that exploits a novel structured map representation
as well as actor-map interactions. Instead of encoding vectorized maps as raster images, we …

Autonomous driving systems require High-Definition (HD) semantic maps to navigate
around urban roads. Existing solutions approach the semantic map** problem by offline …

Geospatial object segmentation, as a particular semantic segmentation task, always faces
with larger-scale variation, larger intra-class variance of background, and foreground …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

Autonomous navigation requires structured representation of the road network and instance-
wise identification of the other traffic agents. Since the traffic scene is defined on the ground …

Extracting roads from satellite imagery is a promising approach to update the dynamic
changes of road networks efficiently and timely. However, it is challenging due to the …

In this paper, we propose PolyTransform, a novel instance segmentation algorithm that
produces precise, geometry-preserving masks by combining the strengths of prevailing …

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited
due to the deployed expensive sensors and time-consuming computation. Camera-based …