Abstract: Systems and techniques are disclosed for predicting the structural status of an object. An object model, such as a machine learning model, can be trained on sample sensor data indicating vibrations, movements, and/or other reactions of objects with known desired and undesired structural statuses to a stimulus agent, such as a puff of air. A scanning device can output a corresponding stimulus agent towards an object, capture sensor data indicating the reaction of the object to the stimulus agent, and provide the sensor data to the trained object model. Based on the sensor data indicating how the object reacted to the stimulus agent, the object model can predict whether the object has a desired structural status or an undesired structural status.

Abstract: Two or more aerial vehicles, such as drones, can each include one or more cameras for capturing surveying or other image data within a field of view. Some of the image data, as well as other data, can be stored and pre-processed on the vehicles, including via onboard edge computing devices. The image and/or other data can be analyzed by a remote system or service, which has additional computing resources, and used to determine the occurrence or existence of an anomaly, such as a seismic event or structure identified in the field of view. The anomaly determination may be based on a disparity, involving a distance between cameras, the cameras' focal length, and a perpendicular distance from a point between cameras to a surface or subsurface point.