Abstract: A Geo-synchronization system involves a video camera in a vehicle, such as a drone, that captures aerial images of an area. The success rate and the accuracy of the geo-synchronization algorithms is improved by using a trained feed-forward Artificial Neural Network (ANN) for identifying dynamic objects, that changes overtime, in frames captured by the video camera. Such frames are tagged, such as by adding metadata. The tagged frames may be used in a geosynchronization algorithm that may be based on comparing with reference images or may be based on another or same ANN, by removing the dynamic object from the fame, or removing the tagged frame for the algorithm. A dynamic object may change over time due to environmental conditions, such as weather changes, or geographical changes. The environmental condition may change is in response to the Earth rotation, the Moon orbit, or the Earth orbit around the Sun.