Abstract: Examples described herein provide a method that includes performing a first scan of an object to generate first scan data. The method further includes detecting a defect on a surface of the object by analyzing the first scan data to identify a region of interest containing the defect by comparing the first scan data to reference scan data. The method further includes performing a second scan of the region of interest containing the defect to generate second scan data, the second scan data being higher resolution scan data than the first scan data. The method further includes combining the first scan data and the second scan data to generate a point cloud of the object.

Abstract: A computer-implemented method is provided that includes causing an aerial vehicle to scan an environment in a predesignated pattern, such that a first set of images are captured. The method further includes detecting an emergency scene in the first set of images of the environment. The method further includes determining locations at which the aerial vehicle is to capture a second set of images of the emergency scene in the environment. The method further includes causing the aerial vehicle to acquire the second set of images at the locations. The method further includes determining selected images of the second set of images focused on the emergency scene. The method further includes extracting the selected images from the second set of images, the selected images comprising a representation of the emergency scene.

Abstract: A computer-implemented method is provided that includes retrieving at least one selected image from a plurality of aerial images of an environment, the at least one selected image comprising surface regions that are concurrently in a three-dimensional (3D) point cloud of the environment. The method further includes detecting areas of the surface regions in the at least one selected image, such that coordinates of the areas of the surface regions are extracted from the at least one selected image. The method further includes comparing the at least one selected image to the 3D point cloud to align common locations in both the at least one selected image and the 3D point cloud. The method further includes displaying an integration of a drawing of the coordinates of the areas of the surface regions in a representation of the 3D point cloud.

Abstract: Examples described herein provide a computer-implemented method that includes receiving a video stream from a camera. The method further includes detecting, within the video stream, an object of interest using a first trained machine learning model. The method further includes, responsive to determining that a confidence score associated with the object of interest fails to satisfy a threshold, determining, using a second trained machine learning model, a direction to move the camera to cause the confidence score to satisfy the threshold. The method further includes presenting an indication of the direction to move the camera to cause the confidence score to satisfy the threshold.

Abstract: Examples described herein provide a method that includes performing a first scan of an object to generate first scan data. The method further includes detecting a defect on a surface of the object by analyzing the first scan data to identify a region of interest containing the defect by comparing the first scan data to reference scan data. The method further includes performing a second scan of the region of interest containing the defect to generate second scan data, the second scan data being higher resolution scan data than the first scan data. The method further includes combining the first scan data and the second scan data to generate a point cloud of the object.