A novel weakly-supervised method based on the segment anything model for seamless transition from classification to segmentation: A case study in segmenting latent photovoltaic locations-Reference-Cited by-同舟云学术

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A novel weakly-supervised method based on the segment anything model for seamless transition from classification to segmentation: A case study in segmenting latent photovoltaic locations

Published:2024-06 Issue: Volume:130 Page:103929
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Yang Ruiqing,He Guojin,Yin Ranyu^ORCID,Wang Guizhou,Zhang Zhaoming,Long Tengfei^ORCID,Peng Yan,Wang Jianping

Publisher

Elsevier BV

Reference62 articles.

1. Destruction from sky: Weakly supervised approach for destruction detection in satellite imagery;Ali;ISPRS J. Photogramm. Remote Sens.,2020

2. Up-to-date literature review on Solar PV systems: Technology progress, market status and R&D;Allouhi;J. Clean. Prod.,2022

3. The potential of remote sensing and GIS in urban building energy modelling;Anand;Energy Built Environ,2023

4. Unsupervised Label Noise Modeling and Loss Correction;Arazo,2019

5. Unlocking the floating photovoltaic potential of Türkiye’s hydroelectric power plants;Ateş;Renew. Energy,2022

Cited by 1 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. A Segment Anything Model based weakly supervised learning method for crop mapping using Sentinel-2 time series images;International Journal of Applied Earth Observation and Geoinformation;2024-09

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