The adaptation of digital technologies in today's world is witnessing vast expansion where mapping tools are no exception. The increasing demand for rapid updates of spatial databases and the need for faster mapping end products are pushing towards using fast and cost-effective technologies. In traditional photogrammetric mapping, accurately calibrated analog metric cameras have been used to capture overlapping film photographs. Metric cameras are designed to provide extremely-high geometric image quality. They employ a low distortion lens system held in a fixed position relative to the film plane. The disadvantage of such systems is the high initial procurement cost and extra processing of photographs before measurements. The advent of low-cost off-the-shelf digital cameras encouraged both researchers and mapping companies to exploit such cameras in the mapping cycle. These cameras should be accurately calibrated and their accuracy and range of application should be determined. Besides photogrammetry, the evolving LIDAR (Light Detection and Ranging) technology provides a new alternative for fast digital mapping. Based on a laser scanner and GPS/INS systems, a LIDAR system produces accurate point cloud measurements of surfaces and sometimes additional intensity images. Typical applications of LIDAR span mapping forestry floors, determination of power line sags, monitoring of coastal zones, city modeling, and construction surveys. The aim of this paper is to investigate the fit between the three mapping alternatives; metric-analog camera, low-cost/non-metric digital camera, and LIDAR Two different types of cameras were used; Wild RC10 photogrammetric camera and Kodak 14n. Each camera was calibrated using a different calibration methodology and the number and arrangement of images taken were also different. As for the LIDAR dataset, an OPTECH ALTM 2050 laser scanner was used. Data from the laser range and reflected intensity were recorded. The comparative performance analysis is based on the quality of fit of the final alignment between the LIDAR and photogrammetric models through check-point analysis and derived orthophotos.