A method for generating training images automatically, the method comprising: receiving information about objects, wherein the information is at least valid at a specific recording time, wherein the information is transmitted by the objects, and wherein the information comprises time dependent information about the objects; receiving an image of an area, wherein the area is imaged at the specific recording time; estimating respective positions of the objects at the specific recording time based on the information; selecting estimated positions of respective objects from the estimated respective positions which fulfill a predefined selection criterion, wherein the criterion comprises an error probability of the estimated positions of respective objects in the received image of the area at the specific recording time; labelling the received image with at least one characteristic of a background of the received image and a Doppler shift of the objects as a state vector with labels that are associated with each object; aligning the received image of the area with the estimated positions of the respective objects; generating training images by separating the imaged area into a first plurality of image tiles and a second plurality of image tiles, wherein each of the first plurality of image tiles differs from each of the second plurality of image tiles, wherein each of the first plurality of image tiles images a respective one or several of the selected positions, and wherein each of the first plurality of image tiles is separated from the imaged area based on each of the first plurality of image tiles comprising the estimated positions of the respective objects; and providing the training images, wherein the training images comprise the first plurality of image tiles and the second plurality of image tiles, wherein the generated training images are provided automatically as an input for a subsequent training of a machine learning tool, the machine learning tool configured to be used for detecting and identifying objects of interest in images autonomously. receiving information about objects, wherein the information is at least valid at a specific recording time, wherein the information is transmitted by the objects, and wherein the information comprises time dependent information about the objects; receiving an image of an area, wherein the area is imaged at the specific recording time; estimating respective positions of the objects at the specific recording time based on the information; selecting estimated positions of respective objects from the estimated respective positions which fulfill a predefined selection criterion, wherein the criterion comprises an error probability of the estimated positions of respective objects in the received image of the area at the specific recording time; labelling the received image with at least one characteristic of a background of the received image and a Doppler shift of the objects as a state vector with labels that are associated with each object; aligning the received image of the area with the estimated positions of the respective objects; generating training images by separating the imaged area into a first plurality of image tiles and a second plurality of image tiles, wherein each of the first plurality of image tiles differs from each of the second plurality of image tiles, wherein each of the first plurality of image tiles images a respective one or several of the selected positions, and wherein each of the first plurality of image tiles is separated from the imaged area based on each of the first plurality of image tiles comprising the estimated positions of the respective objects; and providing the training images, wherein the training images comprise the first plurality of image tiles and the second plurality of image tiles, wherein the generated training images are provided automatically as an input for a subsequent training of a machine learning tool, the machine learning tool configured to be used for detecting and identifying objects of interest in images autonomously.