DFPENet-geology：一种用于同震滑坡高精度识别与分割的深度学习框架
DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides
AI总结：
本文提出DFPENet深度学习框架，融合多尺度图像特征、地质特征与迁移学习，实现仅用RGB图像的高精度同震滑坡识别与分割，并在九寨沟和北海道地震数据上验证了有效性。
摘要：
自动识别与分割方法现已成为识别同震滑坡的基本需求，这对于大规模地震的灾害评估与减灾至关重要。该方法过去通常通过基于像素或面向对象的方式实现。然而，由于遥感数据量巨大、不同地震场景存在差异，以及震后救援对效率的要求，这些方法难以发展为一种准确、快速、全面且具有通用性（跨场景）的同震滑坡识别方案。本文开发了一个稳健的模型——密集特征金字塔编码器-解码器网络（DFPENet），用于理解并融合遥感图像中目标的多尺度特征。所提方法在公开的ISPRS 2D语义数据集上取得了具有竞争力的分割精度。此外，本文提出了一种全面且广泛适用的同震滑坡识别方案，该方案融合了从DFPENet模型中提取的图像特征、地质特征、时间分辨率、滑坡空间分析和迁移学习，同时仅使用RGB图像。为验证其可行性与适用性，利用可获得的震前和震后遥感图像，将该方案应用于中国九寨沟和日本北海道两次地震引发的滑坡。
英文摘要：
Automatic recognition and segmentation methods now become the essential requirement in identifying co-seismic landslides, which are fundamental for disaster assessment and mitigation in large-scale earthquakes. This approach used to be carried out through pixel-based or object-oriented methods. However, due to the massive amount of remote sensing data, variations in different earthquake scenarios, and the efficiency requirement for post-earthquake rescue, these methods are difficult to develop into an accurate, rapid, comprehensive, and general (cross-scene) solution for co-seismic landslide recognition. This paper develops a robust model, Dense Feature Pyramid with Encoder-decoder Network (DFPENet), to understand and fuse the multi-scale features of objects in remote sensing images. The proposed method achieves a competitive segmentation accuracy on the public ISPRS 2D Semantic. Furthermore, a comprehensive and widely-used scheme is proposed for co-seismic landslide recognition, which integrates image features extracted from the DFPENet model, geologic features, temporal resolution, landslide spatial analysis, and transfer learning, while only RGB images are used. To corroborate its feasibility and applicability, the proposed scheme is applied to two earthquake-triggered landslides in Jiuzhaigou (China) and Hokkaido (Japan), using available pre- and post-earthquake remote sensing images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
