A computer-implemented method for determining a change in an object or class of objects in image data, preferably remote sensing data, comprising: receiving a first image data set of a geographical region associated with a first time instance and receiving a second image data set of the geographical region associated with a second time instance; determining an auxiliary probability map on the basis of the first image data set, a pixel in the auxiliary probability map having a pixel value, the pixel value representing a probability that the pixel is associated with an auxiliary object or an auxiliary class of objects, the object forming a part of the auxiliary object or the auxiliary object covering the object in the image data; determining a first object probability map on the basis of the first image data set and the auxiliary probability map and determining a second object probability map on the basis of the second image data set, a pixel in the first and second object probability maps having a pixel value, the pixel value representing a probability that the pixel is associated with the object or class of objects; providing the first object probability map and the second object probability map to an input of a neural network, preferably a recurrent neural network, the neural network being trained to determine a probability of a change in the object or class of objects, based on the pixel values in the first object probability map and in the second object probability map; receiving an output probability map from an output of the neural network, a pixel in the output probability map having a pixel value, the pixel value representing a probability of a change in the object or class of objects; and determining a change in the object or class of objects in the geographical region, based on the output probability map. receiving a first image data set of a geographical region associated with a first time instance and receiving a second image data set of the geographical region associated with a second time instance; determining an auxiliary probability map on the basis of the first image data set, a pixel in the auxiliary probability map having a pixel value, the pixel value representing a probability that the pixel is associated with an auxiliary object or an auxiliary class of objects, the object forming a part of the auxiliary object or the auxiliary object covering the object in the image data; determining a first object probability map on the basis of the first image data set and the auxiliary probability map and determining a second object probability map on the basis of the second image data set, a pixel in the first and second object probability maps having a pixel value, the pixel value representing a probability that the pixel is associated with the object or class of objects; providing the first object probability map and the second object probability map to an input of a neural network, preferably a recurrent neural network, the neural network being trained to determine a probability of a change in the object or class of objects, based on the pixel values in the first object probability map and in the second object probability map; receiving an output probability map from an output of the neural network, a pixel in the output probability map having a pixel value, the pixel value representing a probability of a change in the object or class of objects; and determining a change in the object or class of objects in the geographical region, based on the output probability map. <EOS>