Reconstructing urban areas in 3D out of satellite raster images has been a long-standing and challenging goal of both academical and industrial research. The rare methods today achieving this objective at a Level Of Details 2 rely on procedural approaches based on geometry, and need stereo images and/or LIDAR data as input. We here propose a method for urban 3D reconstruction named KIBS(Keypoints Inference By Segmentation), which comprises two novel features: i-a full deep learning approach for the 3D detection of the roof sections, and ii-only one single (non-orthogonal) satellite raster image as model input. This is achieved in two steps: i-by a Mask R-CNN model performing a 2D segmentation of the buildings' roof sections, and after blending these latter segmented pixels within the RGB satellite raster image, ii-by another identical Mask R-CNN model inferring the heights-to-ground of the roof sections' corners via panoptic segmentation, unto full 3D reconstruction of the buildings and city. We demonstrate the potential of the KIBS method by reconstructing different urban areas in a few minutes, with a Jaccard index for the 2D segmentation of individual roof sections of 88.55% and 75.21% on our two data sets resp., and a height's mean error of such correctly segmented pixels for the 3D reconstruction of 1.60 m and 2.06 m on our two data sets resp., hence within the LOD2 precision range.