LMHLD: A Large-Scale Multisource High-Resolution Landslide Dataset for Landslide Detection Based on Deep Learning

Landslides are among the most common natural disasters globally, posing significant threats to human society. In recent years, deep learning (DL) has been widely applied to rapid landslide detection tasks. However, large-scale, multi-area, and multi-sensor landslide datasets for DL landslide detection are still relatively scarce. Most existing datasets adopt a fixed patch size, overlooking the variations in spatial resolution and landslide scale in remote sensing images, thereby limiting the performance of DL models. To address these limitations, we construct a Large-scale Multi-source High-resolution Landslide Dataset (LMHLD). LMHLD collects remote sensing images from five different satellite sensors, covering seven study areas around the world. LMHLD comprises 25,365 image patches of varying sizes and includes 32,296 annotated landslide instances across diverse geographical environments. Additionally, we propose a Semi-Adaptive Patch Size Selection method (SAPSS), which adaptively selects optimal patch sizes for different study areas. Furthermore, we design a training module, LMHLD<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">part</sub>, which enables the seamless integration of multiple heterogeneous sub-datasets within LMHLD, thereby enhancing the flexibility and robustness of DL models trained on LMHLD. Finally, we demonstrated in four evaluation experiments that LMHLD has the potential to become a benchmark dataset for landslide detection. LMHLD 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. LMHLD is open access and can be accessed through the link: https://doi.org/10.5281/zenodo.11424987.