Cataloging and mapping of landslides rapidly by using an Earth observation-based innovative platform – the Landslide Hunter

Landslides cause severe damage to the built environment and communities, requiring effective hazard management. Landslide catalogs, which provide essential data on past landslide occurrences, are the primary data sources for this purpose. Furthermore, they enable training and validation of predictive landslide models.  Although landslide catalogs are largely compiled through manual mapping based on expert judgement, various advanced techniques using optical Earth observation (EO) imagery have been developed to automate and enhance the creation of such inventories. These methods, however, are mostly tested in specific case studies and they are not put into operation to detect landslides on a regular basis. Moreover, they rely on cloud-free imagery that can be time-consuming to gather, resulting in delays in the timely detection of landslides. This is especially true in regions with frequent rainfall, such as mountainous areas, where landslides are more prevalent.

The Landslide Hunter is a prototype online platform designed to reduce the gap by addressing cloud-cover-related omission in optical imagery, reducing delays in landslide detection, and providing an environment for testing and benchmarking of different EO-based landslide detection methods through a simple plug-and-play method. The platform monitors online resources for events that could trigger landslides, such as major earthquakes, and identifies regions where landslides are likely to have occurred in their aftermath. It then collects and analyzes consecutive optical EO images for these areas to identify visible landslide extents using various landslide detection models, ranging from simple index-based approaches (e.g., NDVI) to advanced machine learning techniques utilizing image segmentation. Proximity to cloud cover is used to assess whether a landslide extent is partially visible, with partial extents being marked for further tracking until complete landslide coverage is achieved through successive analyses. This enables the timely first detection and effective monitoring of landslides, even under cloudy conditions. The results are made available in an open-access landslide catalog through a user-friendly web portal, offering faster updates than traditional catalogs. Users are notified when new landslides are detected, facilitating rapid damage assessment efforts that can ultimately enhance the safety of communities and the built environment.

We present a detailed overview of the design principles and operational framework of the Landslide Hunter platform, highlighting its core features, functionalities, and user interface. We also provide a thorough explanation of the data access methods developed to improve interoperability and ensure seamless integration with other systems.  A live demonstration will illustrate how the platform automatically identifies and tracks landslides under cloudy conditions, enabling timely detection and monitoring of landslide progression.