LAUSR

Landslide inventories are in high demand for risk assessment of this natural hazard, particularly in tropical mountainous regions. This research designed residual networks for landslide detection using spectral (RGB bands) and topographic information (altitude, slope, aspect, curvature). Recent studies indicate that deep learning methods such as convolutional neural networks (CNN) improve landslide mapping results compared to traditional machine learning. But the effects of network architecture designs and data fusion remain largely underexplored in landslide detection. We compared a one-layer CNN with two of its deeper counterparts and residual networks with two fusion strategies (layer stacking and feature-level fusion) to detect landslides in Cameron Highlands, Malaysia. Sixteen different maps were created using proposed methods and evaluated in separate training and testing sub-areas based on overall accuracy, F1-score, and mean intersection over union (mIOU) metrics. When layer stacking is used as a fusion approach, none of the network designs improved landslide detection results. However, our findings showed that when using feature-level fusion, results could be enhanced with the same network designs. Residual networks performed best improving F1-score and mIOU by 0.13 and 12.96%, respectively, using feature-level fusion rather than layer stacking. CNN models also enhanced the detection outcome with the same fusion approach. On single modality datasets, models’ performance varies according to input data, highlighting the effects of input data on network architecture selection. In general, residual networks found to converge faster and generalize better to test areas than other models tested in this research.