探索用于滑坡绘图的深度学习:全面回顾

IF 4.6 3区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
China Geology Pub Date : 2024-04-25 DOI:10.31035/cg2024032
Zhi-qiang Yang , Wen-wen Qi , Chong Xu , Xiao-yi Shao
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引用次数: 0

摘要

详细而准确的滑坡清查图对于定量危害评估和土地规划至关重要。传统的方法依赖于变化检测和面向对象的方法,因其依赖于专家知识和主观因素而饱受诟病。近年来,高分辨率卫星图像的进步,加上人工智能的快速发展,特别是数据驱动的深度学习算法(DL),如卷积神经网络(CNN),为滑坡绘图提供了丰富的特征指标,克服了以往的局限性。在这篇综述论文中,研究人员考察了过去七年中应用于各种环境的 77 种具有代表性的基于 DL 的滑坡检测方法。该研究分析了不同 DL 网络的结构,讨论了五种主要应用场景,并评估了 DL 在地质灾害分析中的进步和局限性。研究结果表明,文章数量的逐年增加反映了人们对人工智能绘制滑坡图的兴趣与日俱增,其中基于 U-Net 的结构因其在特征提取和泛化方面的灵活性而日益突出。最后,我们根据上述研究内容探讨了 DL 在滑坡灾害研究中的阻碍因素。黑箱操作和样本依赖性等挑战依然存在,需要进一步的理论研究和未来在滑坡检测中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring deep learning for landslide mapping: A comprehensive review

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning. Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors. Recent advancements in high-resolution satellite imagery, coupled with the rapid development of artificial intelligence, particularly data-driven deep learning algorithms (DL) such as convolutional neural networks (CNN), have provided rich feature indicators for landslide mapping, overcoming previous limitations. In this review paper, 77 representative DL-based landslide detection methods applied in various environments over the past seven years were examined. This study analyzed the structures of different DL networks, discussed five main application scenarios, and assessed both the advancements and limitations of DL in geological hazard analysis. The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence, with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization. Finally, we explored the hindrances of DL in landslide hazard research based on the above research content. Challenges such as black-box operations and sample dependence persist, warranting further theoretical research and future application of DL in landslide detection.

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来源期刊
China Geology
China Geology GEOLOGY-
CiteScore
7.80
自引率
11.10%
发文量
275
审稿时长
16 weeks
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