Road extraction from high-resolution remote sensing images is a challenging but hot
research topic in the past decades. A large number of methods are invented to deal with this …

Unmanned Aerial Vehicles (UAVs) have emerged as a rapid, low-cost and flexible
acquisition system that appears feasible for application in cadastral map**: high …

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 …

Creating road maps is essential to the success of many applications such as autonomous
driving and city planning. Most approaches in industry focus on leveraging expensive …

This paper deals with semantic segmentation of high-resolution (aerial) images where a
semantic class label is assigned to each pixel via supervised classification as a basis for …

Road network extraction from satellite images often produce fragmented road segments
leading to road maps unfit for real applications. Pixel-wise classification fails to predict …

Delineation of curvilinear structures is an important problem in Computer Vision with
multiple practical applications. With the advent of Deep Learning, many current approaches …

Spatial point processes are mathematical models used to describe and analyse the
geometrical structure of patterns formed by objects that are irregularly or randomly …

We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise
segmentation of (aerial) images and predict objects in a vector representation directly …

Extracting road graphs from aerial images automatically is more efficient and costs less than
from field acquisition. This can be done by a post-processing step that vectorizes road …