Inventories of Multi-type Landslides for Deep Leaning- and Remote Sensing-Based Mapping: A Case of the 2024 Noto Peninsula Events, Japan

This dataset contains two multi-class landslide inventories developed for the 2024 Noto Peninsula earthquake and rainfall events in Ishikawa Prefecture, Japan. The datasets integrate optical (SPOT-6, PlanetScope) imagery with slope for deep learning–based multi-class landslide mapping and analysis. Each dataset covers the entire study area and can be spatially subset for regional applications. Dataset Contents File: Ishikawa_20240518 (Post-earthquake, PlanetScope)This dataset corresponds to the co-seismic landslides following the Jan. 2024 event. It contains six bands: Red band – PlanetScope red reflectance Green band – PlanetScope green reflectance Blue band – PlanetScope blue reflectance NDVI band – Normalized Difference Vegetation Index derived from PlanetScope imagery Slope band – Topographic slope derived from post-earthquake DEM (MLIT) Mask band – Manually labeled multi-class landslide inventory used for model training and validation File: Ishikawa_20241024 (Post-Rainfall, SPOT-6)This dataset corresponds to the post-rainfall imagery acquired after the September 2024 rainfall event. It contains seven bands: Red band – SPOT-6 red reflectance Green band – SPOT-6 green reflectance Blue band – SPOT-6 blue reflectance Near-infrared (NIR) band – SPOT-6 near-infrared reflectance NDVI band – Normalized Difference Vegetation Index derived from red and NIR bands DSM band – Digital Surface Model from dual-angle SPOT-6 imagery Mask band – Manually labeled multi-class landslide inventory used for model training and validation