构建首个多源高分辨率滑坡检测数据集，助力深度学习模型精准识别全球滑坡。
LMHLD: A Large-scale Multi-source High-resolution Landslide Dataset for Landslide Detection based on Deep Learning
- 整合五类卫星影像，覆盖七大地理区域，生成25,365张滑坡样本图块。
- 设计可适配多尺度检测的训练模块，缓解多任务学习中的灾难性遗忘问题。
- 支持跨区域迁移，适合遥感、灾害预警及深度学习研究者使用。
滑坡是全球最常见的自然灾害之一，严重威胁人类社会。深度学习（DL）在大范围灾后快速生成滑坡清单方面已证明有效，但其性能高度依赖高质量标注数据。目前亟需一个基准数据集来评估最新模型的泛化能力。为此，本文构建了基于深度学习的大型多源高分辨率滑坡检测数据集（LMHLD）。该数据集整合了来自五个不同卫星传感器的遥感影像，覆盖全球七个地区：中国汶川（2008）、巴西里约热内卢（2011）、尼泊尔哥尔卡（2015）、中国九寨沟（2015）、中国台湾（2018）、日本北海道（2018）和意大利艾米利亚-罗马涅（2023），共包含25,365个图像块，尺寸各异以适应不同规模滑坡。此外，设计了专用训练模块LMHLDpart，支持多尺度滑坡检测并缓解多任务学习中的灾难性遗忘。通过在其他数据集上的迁移实验，验证了模型的鲁棒性。利用七种U-Net系列模型进行五项数据集质量评估实验，结果表明LMHLD具备成为滑坡检测基准数据集的潜力。该数据集开源可获取（链接：https://doi.org/10.5281/zenodo.11424988），为深度学习模型发展提供坚实基础，有力支持滑坡防治工作。
原文摘要 · Abstract (English)
Landslides are among the most common natural disasters globally, posing significant threats to human society. Deep learning (DL) has proven to be an effective method for rapidly generating landslide inventories in large-scale disaster areas. However, DL models rely heavily on high-quality labeled landslide data for strong feature extraction capabilities. And landslide detection using DL urgently needs a benchmark dataset to evaluate the generalization ability of the latest models. To solve the above problems, we construct a Large-scale Multi-source High-resolution Landslide Dataset (LMHLD) for Landslide Detection based on DL. LMHLD collects remote sensing images from five different satellite sensors across seven study areas worldwide: Wenchuan, China (2008); Rio de Janeiro, Brazil (2011); Gorkha, Nepal (2015); Jiuzhaigou, China (2015); Taiwan, China (2018); Hokkaido, Japan (2018); Emilia-Romagna, Italy (2023). The dataset includes a total of 25,365 patches, with different patch sizes to accommodate different landslide scales. Additionally, a training module, LMHLDpart, is designed to accommodate landslide detection tasks at varying scales and to alleviate the issue of catastrophic forgetting in multi-task learning. Furthermore, the models trained by LMHLD is applied in other datasets to highlight the robustness of LMHLD. Five dataset quality evaluation experiments designed by using seven DL models from the U-Net family demonstrate that LMHLD has the potential to become a benchmark dataset for landslide detection. LMHLD is open access and can be accessed through the link: https://doi.org/10.5281/zenodo.11424988. This dataset provides a strong foundation for DL models, accelerates the development of DL in landslide detection, and serves as a valuable resource for landslide prevention and mitigation efforts.
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