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强化大规模建筑物提取评估：利用代用数据和建筑物匹配开发两级框架

IF 3.7 4区 地球科学 Q2 REMOTE SENSING
European Journal of Remote Sensing Pub Date : 2024-07-03 DOI:10.1080/22797254.2024.2374844
Shenglong Chen, Yoshiki Ogawa, Chenbo Zhao, Yoshihide Sekimoto
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引用次数: 0

摘要

基于深度学习的建筑物提取方法已在多个领域得到广泛应用。然而，对大规模提取结果进行评估仍然具有挑战性，这是因为传统的评估方法无法对建筑物的提取结果进行评估。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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Enhanced large-scale building extraction evaluation: developing a two-level framework using proxy data and building matching
Deep learning-based building extraction methods have widespread applications in diverse fields. However, the evaluation of large-scale extraction results remains challenging, due to traditional eva...
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来源期刊
European Journal of Remote Sensing
European Journal of Remote Sensing REMOTE SENSING-
CiteScore
7.00
自引率
2.50%
发文量
51
审稿时长
>12 weeks
期刊介绍： European Journal of Remote Sensing publishes research papers and review articles related to the use of remote sensing technologies. The Journal welcomes submissions on all applications related to the use of active or passive remote sensing to terrestrial, oceanic, and atmospheric environments. The most common thematic areas covered by the Journal include: -land use/land cover -geology, earth and geoscience -agriculture and forestry -geography and landscape -ecology and environmental science -support to land management -hydrology and water resources -atmosphere and meteorology -oceanography -new sensor systems, missions and software/algorithms -pre processing/calibration -classifications -time series/change analysis -data integration/merging/fusion -image processing and analysis -modelling European Journal of Remote Sensing is a fully open access journal. This means all submitted articles will, if accepted, be available for anyone to read anywhere, at any time, immediately on publication. There are no charges for submission to this journal.
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