Abstract: Forestry timber volume estimations may be provided. In a three-dimensional Digital Surface Model (DSM), a plurality of image data associated with a respective plurality of trees may be identified. Then, from the plurality of image data, species data associated with each of the plurality of trees may be determined. A height above ground may then be determined for each of the plurality of trees by subtracting a height of ground associated with each of the plurality of trees determined from a three-dimensional Digital Terrain Model (DTM) from a height of tree associated with each of the plurality of trees determined from the three-dimensional DSM. A volume associated with each of the plurality of trees may be determined based on the height of tree associated with each of the plurality of trees and the species data associated with each of the plurality of trees.

Abstract: The present disclosure relates to a computer implemented method (100) for attribution of Land Use/Land Cover, LULC, said method comprising the steps of: providing (S1) a plurality of images covering a transport network part, identifying (S2) objects in at least a subset of said plurality of images, identifying (S4) transport routes at least in said at least one satellite image of the plurality of images, and determining (S5) characteristics of said transport network part based on the identified objects at said identified (S4) transport routes.

Abstract: A method for resolution enhancement of images is described comprising the steps of providing (101) at least a first two dimensional (2D) test image, providing (102) a high-resolution 3D map, providing (103) a Machine Learning Network (MLN), extracting (104), from the high-resolution 3D map, a 2D submap, comprising geocoded 2D coordinate data and texture information, extracting (105) a 2D subimage from the 2D test image, which 2D subimage is an image of the same area as the 2D submap, and training the MLN, using the high-resolution 2D submap and the 2D subimage.

Abstract: A method for resolution enhancement of images is described comprising the steps of providing (101) at least a first two dimensional (2D) test image, providing (102) a high-resolution 3D map, providing (103) a Machine Learning Network (MLN), extracting (104), from the high-resolution 3D map, a 2D submap, comprising geocoded 2D coordinate data and texture information, extracting (105) a 2D subimage from the 2D test image, which 2D subimage is an image of the same area as the 2D submap, and training the MLN, using the high-resolution 2D submap and the 2D subimage.