Advances in Landslide Detection through Google Earth Engine: A Comprehensive Review

Landslide is a natural disaster that causes thousands of casualties as well as severe damage to infrastructure because of increased intense precipitation, depleting land resources and rapid urban development. The advancement in technology for landslide detection techniques is improving and inventing. This literature review aims to present a thorough assessment of the advancements in landslide detection by using the Google Earth Engine (GEE). The remote-sensing field has been drastically changed by GEE, which provides extensive archives of satellite data and robust computational resources. The accuracy and feasibility of landslide detection have been greatly improved by combining multitemporal remote-sensing imagery with advanced techniques such as deep convolutional neural networks and spatial–temporal context learning. The review examined numerous approaches, such as spectral and temporal analysis, machine learning techniques, and combining data from different sources. The existing case studies demonstrated how well GEE processes enormous amounts of satellite data, enabling real-time monitoring and providing insightful information for catastrophe preparedness. Despite the advancements, challenges including computing constraints and limits in data resolutions still exist. The analysis emphasizes the need for continued study and development to address these issues and improve the application of GEE in landslide detection. Through capitalizing on the capabilities of GEE, future research can improve the operational usefulness and predictive strength of landslide detection systems, which will be helpful for environmental surveillance and catastrophe management. The valuable insights of this literature review contribute to the knowledge bank, which is useful for research institutions and operational agencies to standardize data-sharing protocols and methodology to increase the consistency and reliability of landslide detection efforts.