Rule-based Learning Techniques to Derive Automated Digital Terrain Model Using Airborne LiDAR Data

Constructing an accurate Digital Terrain Model is costly and time-consuming, leading to more challenges in urban environments due the presence of different objects. This research performs step by analysis LiDAR data using a rule-based algorithm create automatic DTM. method needs no extra has precision equal that DTM, which constructed manually. The DTM this was compared manually investigate accuracy results. It found mean difference between elevations both DTMs rural areas zero 0.10 m, respectively, while slopes 1.2 1.6%, respectively. However, lacked buildings, elevation slope characteristics were equal, revealing identical DTMs, also confirmed sig=.441 from t-test. Although sig=0.0 t-test shows two areas, it does not reveal value difference. Thus, RMSE used examine difference, values ±0.20m, ±0.05m, ±0.04m for urban, rural, without Considering required planning 0.4m, totally acceptable use proposed instead manual method.