Research Projects

Landslides in newly cut terrains during road (renovation or new) construction pose significant challenges in hilly regions, causing loss of life, infrastructure damage, and transportation disruptions. These risks are exacerbated by unstable soils, steep slopes, and heavy rainfall. The proposed project integrates IoT-based sensing, drone imaging, deep learning algorithms, and Vehicle-to-Everything (V2X) communication to develop a robust landslide monitoring and early warning system. IoT sensors will monitor real-time geotechnical parameters like soil moisture and slope deformation, while drones equipped with LiDAR and multi-spectral cameras will capture high-resolution geological images for enhanced terrain analysis. The collected data will be analyzed through advanced deep learning models, including convolutional neural networks (CNNs) and U-Net architectures, for accurate landslide predictions. Field trials and simulated tests along NH-29 (Dimapur to Kohima) will validate system efficiency. The project addresses critical gaps in existing systems, offering a scalable and replicable solution for enhancing landslide prediction and road safety in vulnerable regions.