Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery-Reference-Cited by-同舟云学术

Quantifying and Reducing Registration Uncertainty of Spatial Vector Labels on Earth Imagery

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

He Wenchong¹,Jiang Zhe¹,Kriby Marcus²,Xie Yiqun³,Jia Xiaowei⁴,Yan Da⁵,Zhou Yang⁶

Affiliation:

1. The University of Florida, Gainesville, FL, USA

2. The University of Alabama, Tuscaloosa, AL, USA

3. The University of Maryland, College Park, MD, USA

4. The University of Pittsburgh, Pittsburgh, PA, USA

5. The University of Alabama at Birmingham, Birmingham, AL, USA

6. Auburn University, Auburn, AL, USA

Funder

National Science Foundation

National Oceanic and Atmospheric Administration

the Extreme Science and Engineering Discovery Environment

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539410

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4. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

5. Thomas G Dietterich . 2000. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine learning , Vol. 40 , 2 ( 2000 ), 139--157. Thomas G Dietterich. 2000. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine learning, Vol. 40, 2 (2000), 139--157.

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