167 citations · 206 across the 6 of their papers we have counts for
9 papers · 1 filter
The impact of data volume on performance of deep learning based building rooftop extraction using very high spatial resolution aerial images
Hongjie He, Ke Yang, Yuwei Cai +15
Building rooftop data are of importance in several urban applications and in natural disaster management. In contrast to traditional surveying and mapping, by using high spatial re…
A Deep Learning Approach Based on Graphs to Detect Plantation Lines
Diogo Nunes Gonçalves, Mauro dos Santos de Arruda, Hemerson Pistori +8
Deep learning-based networks are among the most prominent methods to learn linear patterns and extract this type of information from diverse imagery conditions. Here, we propose a…
OpenGF: An Ultra-Large-Scale Ground Filtering Dataset Built Upon Open ALS Point Clouds Around the World
Nannan Qin, Weikai Tan, Lingfei Ma +2
Ground filtering has remained a widely studied but incompletely resolved bottleneck for decades in the automatic generation of high-precision digital elevation model, due to the dr…
UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks
Weikai Tan, Dedong Zhang, Lingfei Ma +3
Stack interchanges are essential components of transportation systems. Mobile laser scanning (MLS) systems have been widely used in road infrastructure mapping, but accurate mappin…
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Ying Li, Lingfei Ma, Zilong Zhong +4
Recently, the advancement of deep learning in discriminative feature learning from 3D LiDAR data has led to rapid development in the field of autonomous driving. However, automated…
Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
Weikai Tan, Nannan Qin, Lingfei Ma +5
Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definitio…