Rapid damage assessment after an earthquake is vital for an efficient emergency response. With the rapid development of unmanned aerial vehicles (UAVs), they can now be used to rapidly assess the building damage and have the advantages of real-time operation, flexibility, low cost, etc. However, UAV images are “big data,” and UAVs can obtain hundreds of scene images in a short period of time. It is, therefore, important to speed up the processing time for UAV images. This paper proposes a parallel processing approach for accelerating the speed of automatic three-dimensional (3-D) building damage detection, using a preseismic digital topographical map and postseismic UAV images. From the experimental results obtained from 3-D building damage detection of the 2013 Ya’an earthquake in Baoxing County, Sichuan province of China, the comparison of the parallel processing in terms of digital surface model generation using postseismic UAV images shows that the total cost of the multicores central processing units (CPUs) and graphics processing unit-based implementation is about 11.0 times faster than the single-core CPU implementation.

Integral field spectroscopy (IFS) is capable of simultaneously collecting two-dimensional high-resolution spatial image information and spectral information in a target field of view (FOV). The image slicer is the key element in IFS for segmenting the target FOV. Current micro-image slicer fabrication methods such as molecular assembling need strictly accurate control during sticking, which is time-consuming and expensive. In this study, we improved a direct-adhesive method for thin glass slicer manufacture and alignment. Thin glass is fabricated via single polishing and direct stitching to form a micro-image slicer. Based on charge coupled device (CCD) camera tests on parallel light, the manufacture accuracy for a six-channel image slicer reaches that of the molecular assembling scheme. This method can be applied to the fabrication of image slicers with different apertures.