Abstract: The present disclosure relates to a method for 3D reconstruction from satellite imagery using deep learning, said method comprising providing (101) at least two overlapping 2D satellite images, providing (102) imaging device parameters for the at least two overlapping 2D satellite images, providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view geocoded 3D ground truth data and predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters.

Abstract: The present disclosure relates to a method for 3D reconstruction from satellite imagery using deep learning, said method comprising providing (101) at least two overlapping 2D satellite images, providing (102) imaging device parameters for the at least two overlapping 2D satellite images, providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view geocoded 3D ground truth data and predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters.

Abstract: The present disclosure relates to a method for 3D reconstruction from satellite imagery using deep learning, said method comprising providing (101) at least two overlapping 2D satellite images, providing (102) imaging device parameters for the at least two overlapping 2D satellite images, providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view geocoded 3D ground truth data and predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters.

Abstract: The present disclosure relates to a method for 3D reconstruction from satellite imagery using deep learning, said method comprising providing (101) at least two overlapping 2D satellite images, providing (102) imaging device parameters for the at least two overlapping 2D satellite images, providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view geocoded 3D ground truth data and predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters.

Abstract: The present disclosure relates to a method for 3D reconstruction from satellite imagery using deep learning, said method comprising providing (101) at least two overlapping 2D satellite images, providing (102) imaging device parameters for the at least two overlapping 2D satellite images, providing (103) at least one trained Machine Learning Network, MLN, able to predict depth maps, said trained MLN being trained on a training set comprising multi-view geocoded 3D ground truth data and predicting (104) a depth map of the at provided at least two 2D satellite images using the trained at least one MLN and based on the corresponding imaging device parameters.

Abstract: A method for classifying a terrain type in an area is provided, which comprises the steps of obtaining a plurality of overlapping aerial images of the area, calculating at least one terrain type index for each part of each of the aerial images which lies in the area, where the at least one terrain type index represents the terrain type, determining at least one terrain type index for each part of the area based on the calculated at least one terrain type index for each part of each of the aerial images, and classifying the parts of the area for which at least one pre-determined conditions is met as containing the terrain type, wherein at least one of the at least one predetermined condition relates to a value of the determined at least one terrain type index. Also provided is a system and a computer program product.

Abstract: A method for classifying a terrain type in an area is provided, which comprises the steps of obtaining a plurality of overlapping aerial images of the area, calculating at least one terrain type index for each part of each of the aerial images which lies in the area, where the at least one terrain type index represents the terrain type, determining at least one terrain type index for each part of the area based on the calculated at least one terrain type index for each part of each of the aerial images, and classifying the parts of the area for which at least one pre-determined conditions is met as containing the terrain type, wherein at least one of the at least one predetermined condition relates to a value of the determined at least one terrain type index. Also provided is a system and a computer program product.

Abstract: A method for classifying a terrain type in an area is provided, which method comprises the steps of: obtaining a plurality of overlapping aerial images of the area; calculating at least one terrain type index for each part of each of the aerial images which lies in the area, where the at least one terrain type index represents the terrain type; determining at least one terrain type index for each part of the area based on the calculated at least one terrain type index for each part of each of the aerial images; and classifying the parts of the area for which at least one pre-determined conditions is met as containing the terrain type, wherein at least one of the at least one predetermined condition relates to a value of the determined at least one terrain type index. An associated system and computer program product are also provided.

Abstract: A method for classifying a terrain type in an area is provided, which method comprises the steps of: obtaining a plurality of overlapping aerial images of the area; calculating at least one terrain type index for each part of each of the aerial images which lies in the area, where the at least one terrain type index represents the terrain type; determining at least one terrain type index for each part of the area based on the calculated at least one terrain type index for each part of each of the aerial images; and classifying the parts of the area for which at least one pre-determined conditions is met as containing the terrain type, wherein at least one of the at least one predetermined condition relates to a value of the determined at least one terrain type index. An associated system and computer program product are also provided.