Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network

Author:

Huang Jianfeng,Zhang Xinchang,Xin QinchuanORCID,Sun Ying,Zhang Pengcheng

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province, China

National Administration of Surveying, Mapping and Geoinformation of China

Research Institute of Henan Spatio-Temporal Big Data Industrial Technology

Key Projects for Young Teachers at Sun Yat-sen University

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference53 articles.

1. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks;Alshehhi;ISPRS J. Photogramm. Remote Sens.,2017

2. Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks;Audebert;ISPRS J. Photogramm. Remote Sens.,2017

3. Automatic detection of residential buildings using LIDAR data and multispectral imagery;Awrangjeb;ISPRS J. Photogramm. Remote Sens.,2010

4. SegNet: a deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Trans. Pattern Anal. Mach. Intell.,2017

5. Bischke, B., Helber, P., Folz, J., Borth, D., Dengel, A., 2017. Multi-task learning for segmentation of building footprints with deep neural networks. arXiv preprint arXiv:1709.05932.

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