A method comprising: providing to a trained neural network a first image, wherein the first image a) is captured by an image capture device at a first location and at a first pose, b) is associated with geophysical coordinates corresponding to the first location, and c) comprises one or more static features (62a;62b); providing to the trained neural network a second image, wherein the second image a) is captured by an image capture device at a second location and at a second pose, b) is associated with geophysical coordinates corresponding to the second location, and c) comprises at least one of the one or more static features (62a;62b), wherein a static feature (62a; 62b) is a building facade, a sign, a lane line, a lamp post, a street lamp, a telephone pole, a guard rail, a mile marker or a pavement; identifying the at least one of the one or more static features (62a;62b) in both the first and second images with a branch of the trained neural network ; generating a three dimensional image comprising the at least one identified static feature (62a;62b) and based on the first and second images; extracting a three dimensional geometry of the at least one identified static feature (62a;62b); encoding the extracted three dimensional geometry as an array; based on the first location and second location, determining a static feature location for the at least one identified static feature (62a;62b), wherein the geophysical coordinates corresponding to the first location and the geophysical coordinates corresponding to the second location are determined by a location sensor of the image capture device , and wherein the location sensor is a GPS sensor; and storing the array in association with a map tile, wherein the map tile is selected based on the static feature location. providing to a trained neural network a first image, wherein the first image a) is captured by an image capture device at a first location and at a first pose, b) is associated with geophysical coordinates corresponding to the first location, and c) comprises one or more static features (62a;62b); providing to the trained neural network a second image, wherein the second image a) is captured by an image capture device at a second location and at a second pose, b) is associated with geophysical coordinates corresponding to the second location, and c) comprises at least one of the one or more static features (62a;62b), wherein a static feature (62a; 62b) is a building facade, a sign, a lane line, a lamp post, a street lamp, a telephone pole, a guard rail, a mile marker or a pavement; identifying the at least one of the one or more static features (62a;62b) in both the first and second images with a branch of the trained neural network ; generating a three dimensional image comprising the at least one identified static feature (62a;62b) and based on the first and second images; extracting a three dimensional geometry of the at least one identified static feature (62a;62b); encoding the extracted three dimensional geometry as an array; based on the first location and second location, determining a static feature location for the at least one identified static feature (62a;62b), wherein the geophysical coordinates corresponding to the first location and the geophysical coordinates corresponding to the second location are determined by a location sensor of the image capture device , and wherein the location sensor is a GPS sensor; and storing the array in association with a map tile, wherein the map tile is selected based on the static feature location. <EOS>