A method for registration of two and three-dimensional (3-D) images, the method comprising: scanning the geographical region by a LIDAR system attached to a LIDAR drone to produce a 3-dimensional point cloud comprising 3-D cloud image points thereof; wherein the 3-dimensional point cloud is calibrated into world coordinates or geo-registered; generating a number of 2-D height slices at differing heights of the 3-dimensional point cloud comprising 3-D coordinate and intensity measurements of the geographical region; extracting slice object shape morphology from the height slices; labeling the slice object shape morphology; extracting slice shape properties from the slice object shape morphology; capturing a 2-D image comprising 2-D image points of a geographical region by a camera attached to a camera drone ; transforming the 2-dimensional (2-D) image of the geographical region via a regional maxima transform or an edge segmenting and boundary filling (ESBF) transform to produce a filtered 2-D image; iteratively eroding and opening the filtered 2-D image to produce a processed electro-optical (EO) 2-D image; extracting 2-D object shape morphology from the processed EO 2-D image; labelling the 2-D object shape morphology; extracting 2-D shape properties from the 2-D object shape morphology; wherein the extracted 2-D shape properties comprise locations of corners of the shapes and descriptions of the shapes; constellation matching the 2-D image to the height slices based on the 2-D shape properties and the slice shape properties; determining matching points between the 2-D image points and the 3-D cloud image points; estimating a pose position of the camera based on the matching points to provide an estimated pose position; determining a geo-location of the camera drone based on the estimated pose position; and painting the point cloud image with colors from the 2-D image with a process called fusion. scanning the geographical region by a LIDAR system attached to a LIDAR drone to produce a 3-dimensional point cloud comprising 3-D cloud image points thereof; wherein the 3-dimensional point cloud is calibrated into world coordinates or geo-registered; generating a number of 2-D height slices at differing heights of the 3-dimensional point cloud comprising 3-D coordinate and intensity measurements of the geographical region; extracting slice object shape morphology from the height slices; labeling the slice object shape morphology; extracting slice shape properties from the slice object shape morphology; capturing a 2-D image comprising 2-D image points of a geographical region by a camera attached to a camera drone ; transforming the 2-dimensional (2-D) image of the geographical region via a regional maxima transform or an edge segmenting and boundary filling (ESBF) transform to produce a filtered 2-D image; iteratively eroding and opening the filtered 2-D image to produce a processed electro-optical (EO) 2-D image; extracting 2-D object shape morphology from the processed EO 2-D image; labelling the 2-D object shape morphology; extracting 2-D shape properties from the 2-D object shape morphology; wherein the extracted 2-D shape properties comprise locations of corners of the shapes and descriptions of the shapes; constellation matching the 2-D image to the height slices based on the 2-D shape properties and the slice shape properties; determining matching points between the 2-D image points and the 3-D cloud image points; estimating a pose position of the camera based on the matching points to provide an estimated pose position; determining a geo-location of the camera drone based on the estimated pose position; and painting the point cloud image with colors from the 2-D image with a process called fusion. <EOS>