Large quantities of aerial and satellite images are being acquired on a daily basis. Many practical applications may benefit from the analysis of such huge amounts of data. We propose an automated content-based analysis of aerial photography in this letter, which may be used to identify and label arbitrary objects or areas in high-resolution pictures. We developed a convolutional neural network-based approach for automated object identification for this purpose. In the tasks of aerial picture classification and object identification, a new two-stage method for network training is developed and validated. First, we used the UC Merced data set of aerial pictures to evaluate the suggested training method, and we were able to obtain an accuracy of about 98.6%. Second, a technique for automatically detecting objects was developed and tested. For GPGPU implementation, a processing time of approximately 30 seconds was needed for one aerial picture of size 5000 x 5000 pixels.