Urban surface classification using semi-supervised domain adaptive deep learning models and its application in urban environment studies-Reference-Cited by-同舟云学术

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Urban surface classification using semi-supervised domain adaptive deep learning models and its application in urban environment studies

Published:2023-11-21 Issue:59 Volume:30 Page:123507-123526
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Ding Xiaotian,Fan Yifan,Li Yuguo,Ge Jian

Funder

the National Natural Science Foundation of China

“Pioneer” and “Leading Goose” R&D Program of Zhejiang

Zhejiang University Global Partnership Fund

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-023-30843-8.pdf

Reference73 articles.

1. Aryal J, Neupane B (2023) Multi-Scale Feature Map Aggregation and Supervised Domain Adaptation of Fully Convolutional Networks for Urban Building Footprint Extraction. Remote Sens 15:488. https://doi.org/10.3390/rs15020488

2. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615

3. Chen W, Zhou Y, Wu Q et al (2020) Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sens 12:2805. https://doi.org/10.3390/rs12172805

4. Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv preprint arXiv:1706.05587. https://doi.org/10.48550/arXiv.1706.05587

5. Chen Y, Li W, Sakaridis C, et al (2018) Domain adaptive faster R-CNN for object detection in the Wild. Proceedings of the IEEE conference on computer vision and pattern recognition 3339–3348. https://doi.org/10.48550/arXiv.1803.03243

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