A typical human eye will respond to wavelengths from approximately 400 to 700 nm. A hyperspectral camera can extend the wavelength to as high as 2500 nm. This extension will allow engineers to find objects, identify materials, and detect processes on structural surface, which cannot be done with visual inspection.

This project aims to develop an open-source catalogue of concrete and steel surfaces and their spectral/spatial features (discoloration, characteristic wavelength, roughness, texture, shape, etc.), extract spatial/spectral features of hyperspectral images, and develop/train a multi-class classification or regression classifier through machine learnings (supervised and/or semi-supervised), and validate the classifier as a decision-making tool for the assessment of concrete crack and degradation processes, in-situ concrete properties, and corrosion process in steel bridges.