  In road monitoring, it is an important issue to detect changes in the road
surface at an early stage to prevent damage to third parties. The target of the
falling object may be a fallen tree due to the external force of a flood or an
earthquake, and falling rocks from a slope. Generative deep learning is
possible to flexibly detect anomalies of the falling objects on the road
surface. We prototype a method that combines auto-encoding reconstruction and
isolation-based anomaly detector in application for road surface monitoring.
Actually, we apply our method to a set of test images that fallen objects is
located on the raw inputs added with fallen stone and plywood, and that snow is
covered on the winter road. Finally we mention the future works for practical
purpose application.
