A method for training a convolutional neural network, CNN, for detecting a Point Of Interest, POI, change in a pair of inputted POI images, wherein a POI change corresponds to the replacement of a POI at a particular location by another one over a period of time defined by the pair of inputted POI images; the method comprises the implementation of steps of (a) generating, by a data processor of a first server , a base of training images, wherein said generating comprises: (a1) obtaining an initial set of labelled POI images; (a2) for each image from a subset of said initial set, identifying a signage region in said image, said signage region depicting a key visual feature of a POI depicted by the image, wherein said key visual feature of a POI is a name and/or a logo of the POI, the signage region being identified using Optical Character Recognition and/or logo detection; (a3) generating at least one synthetic POI image corresponding to a first image of said subset wherein the signage region has been replaced by that of a second image of said subset, and associating to said synthetic POI image the label of the second image; (a4) providing as the base of training images a final set of POI images comprising the initial set of POI images and each generated synthetic POI image; (b) generating, by a data processor of a second server , a plurality of triplets of training images from the base of training images, each triplet comprising a pair of related POI images, and a pair of unrelated POI images, two POI images being determined as related or not on the basis of their labels; (c) training, by the data processor of the second server , from said plurality of triplets, a CNN of the three-stream Siamese type. (a) generating, by a data processor of a first server , a base of training images, wherein said generating comprises: (a1) obtaining an initial set of labelled POI images; (a2) for each image from a subset of said initial set, identifying a signage region in said image, said signage region depicting a key visual feature of a POI depicted by the image, wherein said key visual feature of a POI is a name and/or a logo of the POI, the signage region being identified using Optical Character Recognition and/or logo detection; (a3) generating at least one synthetic POI image corresponding to a first image of said subset wherein the signage region has been replaced by that of a second image of said subset, and associating to said synthetic POI image the label of the second image; (a4) providing as the base of training images a final set of POI images comprising the initial set of POI images and each generated synthetic POI image; (a1) obtaining an initial set of labelled POI images; (a2) for each image from a subset of said initial set, identifying a signage region in said image, said signage region depicting a key visual feature of a POI depicted by the image, wherein said key visual feature of a POI is a name and/or a logo of the POI, the signage region being identified using Optical Character Recognition and/or logo detection; (a3) generating at least one synthetic POI image corresponding to a first image of said subset wherein the signage region has been replaced by that of a second image of said subset, and associating to said synthetic POI image the label of the second image; (a4) providing as the base of training images a final set of POI images comprising the initial set of POI images and each generated synthetic POI image; (b) generating, by a data processor of a second server , a plurality of triplets of training images from the base of training images, each triplet comprising a pair of related POI images, and a pair of unrelated POI images, two POI images being determined as related or not on the basis of their labels; (c) training, by the data processor of the second server , from said plurality of triplets, a CNN of the three-stream Siamese type. <EOS>