Multi-scale landslide classification using optical satellite imagery　– Case study using SPOT satellite data and Random Forest classification –

Abstract

This study proposes a landslide classification method based on multi-scale spatial analysis of optical satellite imagery, focusing on the 2018 Eastern Iburi Earthquake in Hokkaido, Japan. A multi-scale spatial analysis framework was constructed by integrating mesh sizes of 0.5 km, 1 km, and 2 km, utilizing pixel- level data from SPOT6/7 satellite bands (red, green, blue, near-infrared) and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) as explanatory variables. The model was trained to predict the presence or absence of slope failure.

To assess the effectiveness of the proposed method, classification performance was compared between single-scale and multi-scale analyses. The results indicated that incorporating multi-scale features reduced misclassification—namely, false positives and false negatives—and led to an improvement in overall classi- fication accuracy.

These findings demonstrate that feature design accounting for spatial context across multiple scales can enhance the accuracy of landslide detection using optical satellite imagery.