Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Machine unlearning is a process of removing the impact of some training data from the
machine learning (ML) models upon receiving removal requests. While straightforward and …

This review article collects knowledge on the use of eye-tracking and machine learning
methods for application in automated and interactive geovisualization systems. Our focus is …

Inferring road graphs from satellite imagery is a challenging computer vision task. Prior
solutions fall into two categories:(1) pixel-wise segmentation-based approaches, which …

Digital maps with lane-level details are the foundation of many applications. However,
creating and maintaining digital maps especially maps with lane-level details, are labor …

The task of road extraction has aroused remarkable attention due to its critical role in
facilitating urban development and up-to-date map maintenance, which has widespread …

We consider the problem of traffic accident analysis on a road network based on road
network connections and traffic volume. Previous works have designed various deep …

Road surface extraction from high-resolution remote sensing images has many engineering
applications; however, extracting regularized and smooth road surface maps that reach the …

Mountain roads are of great significance to traffic navigation and military road planning.
Extracting mountain roads based on high-resolution remote sensing images (HRSIs) is a hot …