Multi Deep Learning Model for Building Footprint Extraction from High Resolution Remote Sensing Image
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-3394-3_29
Reference7 articles.
1. Emek, R.A., Demir, N.: Building detection from SAR images using UNET deep learning method, pp. 215–218 (2020). https://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-215-2020
2. We, X., et al.: Building outline extraction directly using the u2-net semantic segmentation model from high-resolution aerial images and a comparison study. Remote. Sens. 13, 3187 (2021)
3. Zhao, K., Kang, J., Jung, J., Sohn, G.: Building extraction from satellite images using mask R-CNN with building boundary regularization. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 242–2424 (2018). https://doi.org/10.1109/CVPRW.2018.00045
4. Qinzhe, H., Yin, Q., Zheng, X., Chen, Z.: Remote sensing image building detection method based on mask r-cnn. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00322-z
5. USGS: Sunnyvale uav images. https://earthexplorer.usgs.gov/
Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. The segmentation and intelligent recognition of structural surfaces in borehole images based on the U2-Net network;PLOS ONE;2024-03-07
2. A framework for controllable Pareto front learning with completed scalarization functions and its applications;Neural Networks;2024-01
3. Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning;2023 12th International Conference on Awareness Science and Technology (iCAST);2023-11-09
4. An Approach to Extract Information from Academic Transcripts of HUST;Intelligent Systems and Networks;2023
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3