Accuracy evaluation of a three-dimensional spatial modeling approach to model linear objects and predict their lengths

Real world objects are three-dimensional. Many current geographic information systems (GISs) only represent two-dimensional information. The GIS community has been struggling with solving complex problems dealing with 3-D objects using a 2-D approach.; This research focused on modeling linear objects in a 3-D space, predicting their 3-D distances, and evaluating their accuracy. A point model was developed to represent a 3-D line with a group of 3-D points (with X/Y/Z coordinates) connected by straight lines. It required two input datasets: an elevation dataset and a planimetric line dataset. With elevation datasets in different formats (point data and digital elevation models (DEMs)), two approaches were proposed, differing in how the third dimension (elevation) was introduced. With point data, a snapping approach was developed. With DEMs, elevations for points uniformly distributed along planimetric lines were obtained via bilinear interpolations. Mathematical equations were derived to predict 3-D distances.; The developed 3-D model and algorithm were tested in a transportation case study. Two elevation datasets were used: LIDAR (point clouds and DEMs) and national elevation dataset (NED). The accuracy of predicted 3-D distances was evaluated by comparing them to distance measurement instrument (DMI) measured distances. The effects from the use of different elevation datasets and intervals on the accuracy were evaluated via a sensitivity analysis. The effects from the geometric properties of linear objects on the accuracy were evaluated via significant factor analyses. The usefulness of this research was demonstrated by applying the resulting 3-D road centerline data in assessing highway flooding.; The key finding from this research is that using LIDAR point data in the creative snapping approach improved the accuracy by 28% and using LIDAR DEMs improved the accuracy by 6%, compared to using NED data. This research recommends adopting the 3-D approach developed herein to calculate 3-D distances for roads to eliminate the need for field measurement.