Recent advances in sensing technologies provide opportunity to utilize advanced data collection equipment such as laser scanners, high-resolution cameras, etc. for both short- and long-time monitoring of structures. Laser scanners, which are capable of collecting up-to two million points per second along with high-resolution images, are especially evolving rapidly and their usage for capturing and documenting the current condition of varying structural types is becoming increasingly feasible. The authors? previous research has focused on developing generalized algorithms for detecting surface damage on structures from captured laser scans and images. These algorithms are capable of automatically detecting surface damage on varying structural types as well as several construction materials since they use underlying surface geometry for performing damage detection. These damage types include concrete cracking, concrete spalling, steel delamination, steel section loss, bent members, and other misalignments. This paper investigates the use of supervised learning methods for determining whether a detected region represents an actual damage location. First, the input feature representations of the learning algorithms for each damage type are determined and the associated training sets, which are representative of real-world use of the algorithms, are prepared. Once the training and validation are completed, the learning algorithms are tested on the detected damage data, which is obtained by running the damage detection algorithms on both laser scans and texture-mapped images from a concrete test setup. Finally, the accuracy of these learning algorithms is investigated.