Volumetric estimation of landslide-induced terrain change using conditional GAN with multi-temporal DEMs and satellite imagery

• Develop a cGAN model to predict DoD for topographic changes. • Estimate landslide volume beyond V–A empirical models. • Enable 3D landslide mapping without post-event DEMs. • Reveal only 15.95% overlap between DoD and inventory. • Improve DoD prediction via fused terrain and spectral data. Estimating landslide volumes remains challenging because of the limited availability of high-resolution terrain data. Traditional methods often rely on empirical area-to-volume conversion formulas, which introduce significant uncertainties due to terrain variability and simplified assumptions. This study proposes a deep learning framework that integrates multitemporal satellite imagery, LiDAR-derived Digital Elevation Models (DEMs), terrain attributes, and a conditional generative adversarial network (cGAN) to simulate the DEM of Difference (DoD) and estimate landslide-induced volumetric changes. The input DEMs were resampled to 20-meter resolution for publicly available coverage exceeding 78,183 km 2 . A total of 2,881 map frames contained two DEM epochs, and 348 frames had three epochs, with time intervals typically ranging from 5 to 8 years. From these, 198 deep-seated landslide cases were extracted for analysis, covering approximately 7.12 km 2 , including 4.89 km 2 of erosion area and 2.23 km 2 of deposition area. The proposed model achieved an overall classification accuracy of 0.66, with F1-scores of 0.46 for erosion, 0.30 for deposition, and 0.78 for background. Volumetric estimations revealed a consistent underestimation trend, with median erosion errors of approximately − 50 % and deposition errors approaching − 100 % across the five-fold cross-validation. The framework effectively captures three-dimensional spatial distributions and enables accurate volumetric estimation without the need for post-event DEMs, offering a practical solution for data-scarce regions. Additionally, it enhances sediment volume assessments that are crucial for disaster prevention and sediment management, bridging the gap between empirical estimation and modern deep learning techniques.