Abstract: Example implementations described herein can be directed to detecting a human disturbance from sensor data streamed from one or more sensors in a network of sensors; processing the detected human disturbance to determine a probability of a chainsaw event (e.g., light chainsaw or dense chainsaw event) and an estimated lead time for the chainsaw event for an area associated with the one or more sensors; and determining, for neighboring sensors to the one or more sensors in the network of sensors, a probability of a change of state to the human disturbance or the chainsaw event for other areas associated with the neighboring sensors.