Assessing Deep Learning Segmentation Models for Mapping Landslides From LiDAR‐Based Elevation Data
Year: 2026
Type: article
Abstract: Robust landslide inventories are critical for characterizing landslide hazard and helping reduce societal vulnerability. For this study, LiDAR‐derived digital elevation models (DEMs), their derivatives, and a landslide inventory were used to train different deep learning segmentation models to assess the functionality of automatic segmentation for high‐resolution, non‐event specific landslide inventories. Three deep learning segmentation approaches (U‐Net, MA‐Net, and FPN) were trained with a variety of hyperparameters, with some consideration of input modalities. Models were assessed with a spatial test holdout and then further evaluated against a proximal holdout landslide inventory. In total, 90 model scenarios were trained. Model performance metrics and visual assessment suggested that in general, model performances varied only slightly during hyperparameter tuning. U‐Net trained on DEM gradients performed the best, with a feature IoU (intersection over union) of 0.407 among all scenarios. MA‐Net trained on DEM gradients had the best performance against the proximal holdout inventory, with an IoU of 0.398, however these performances are within random variability of the models indicating no clear best model. A comparison of the proximal holdout inference results from various trained models suggested that deep learning segmentation models are effective in mapping landslides from LiDAR‐derived DEMs. A well‐trained landslide segmentation model could potentially be used to aid manual mapping by improving efficiency and accuracy. The direct inference result could also be used as an approximate inventory for use in data‐driven models, such as landslide susceptibility models, to significantly reduce the time and resources required for traditional landslide mapping.
Source: Earth and Space Science
DOI: 10.1029/2025ea004937
Language: English
Subfield: Management, Monitoring, Policy and Law
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,650
OpenAlex ID: W7218693173