H
Haiyan Guan
Researcher at Nanjing University of Information Science and Technology
Publications - 106
Citations - 3439
Haiyan Guan is an academic researcher from Nanjing University of Information Science and Technology. The author has contributed to research in topics: Point cloud & Computer science. The author has an hindex of 23, co-authored 87 publications receiving 2189 citations. Previous affiliations of Haiyan Guan include Nanjing University & Wuhan University.
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End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++
TL;DR: A novel end-to-end CD method based on an effective encoderdecoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets, which outperforms the other state-of-the-art CD methods.
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Using mobile laser scanning data for automated extraction of road markings
TL;DR: Wang et al. as mentioned in this paper proposed a curb-based method for road surface extraction from mobile laser scanning (MLS) point clouds, which first partitions the raw MLS data into a set of profiles according to vehicle trajectory data, and then extracts small height jumps caused by curbs in the profiles via slope and elevation difference thresholds.
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Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds
TL;DR: The results show that road surfaces are correctly segmented, and street light poles are robustly extracted with a completeness exceeding 99%, a correctness exceeding 97%, and a quality exceeding 96%, thereby demonstrating the efficiency and feasibility of the proposed algorithm to segment road surfaces and extract street light pole from huge volumes of mobile LiDAR point-clouds.
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Use of mobile LiDAR in road information inventory: a review
TL;DR: This review presents a more in-depth description of current mobile LiDAR studies on road information inventory, including the detection and extraction of road surfaces, small structures on the road surfaces and pole-like objects.
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Deep learning-based tree classification using mobile LiDAR data
TL;DR: Comparative experiments demonstrate that the uses of waveform representation and deep Boltzmann machines contribute to the improvement of classification accuracies of tree species.