Landslide extraction using a novel empirical method and binary semantic segmentation U-NET framework using sentinel-2 imagery

IF 1.4 4区地球科学 Q3 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

Remote Sensing Letters Pub Date : 2024-02-29 DOI:10.1080/2150704x.2024.2320178

Meghanadh Devara, Vipin Kumar Maurya, Ramji Dwivedi

引用次数: 0

Abstract

Artificial intelligence (AI) has achieved a remarkable place in solving complex problems in almost all disciplines. Based on the recent notable performances of machine learning and deep learning te...

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利用新颖的经验方法和二元语义分割 U-NET 框架，利用哨兵-2 图像提取滑坡信息

人工智能（AI）在解决几乎所有学科的复杂问题方面都取得了令人瞩目的成就。基于机器学习和深度学习技术最近的显著表现，人工智能已成为解决复杂问题的重要工具。

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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来源期刊

Remote Sensing Letters REMOTE SENSING-IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY

CiteScore

4.10

自引率

4.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Remote Sensing Letters is a peer-reviewed international journal committed to the rapid publication of articles advancing the science and technology of remote sensing as well as its applications. The journal originates from a successful section, of the same name, contained in the International Journal of Remote Sensing from 1983 –2009. Articles may address any aspect of remote sensing of relevance to the journal’s readership, including – but not limited to – developments in sensor technology, advances in image processing and Earth-orientated applications, whether terrestrial, oceanic or atmospheric. Articles should make a positive impact on the subject by either contributing new and original information or through provision of theoretical, methodological or commentary material that acts to strengthen the subject.