Can AI-generated landslide inventories replace humans' cognitive abilities in hazard and risk scenarios?

Landslide inventories are quintessential for landslide susceptibility mapping, hazard modeling, and risk management. Experts and organizations all across the world have preferred manual visual interpretation of satellite and aerial imagery for decades. However, there are other issues with manual inventory, such as the subjective process of manually extracting landslide boundaries, the lack of sharing landslide polygons within the geoscientific community, and the amount of time and effort engaged in the inventory generation process by the expert interpreters. To address these challenges, a large amount of research on semi-automated and automatic mapping of landslide inventories has been conducted in recent years. The automatic development of landslide inventory using Artificial Intelligence (AI) approaches is still in its early stages, as there is currently no published study that can generate a ground truth representation of a landslide situation following a landslide-triggering event. In terms of landslide boundary delineation utilizing AI-based models, the evaluation metrics in recent research suggest a range of 50-80% of the F1-score. However, with the exception of those using model evaluation testing in the same studied area, very few studies claim to have attained more than 80% F1 score, that too at larger scales of investigation. As a result, there is currently a research gap between the generation of AI-based landslide inventory and their applicability for landslide hazard and risk assessments. There is a need to advocate for the geoscientific community to check the reliability of AI-generated landslide data in terms of their usage in the succeeding phases of landslide response and mitigation in impacted areas.