How to Generate a Digital Terrain Model from a LiDAR Point Cloud: Complete Workflow

How to Generate a Digital Terrain Model from a LiDAR Point Cloud: Complete Workflow

May 8, 2025

Digital Terrain Model (DTM) generation  from LiDAR point clouds is a fundamental process in geospatial engineering, supporting applications such as flood modeling, infrastructure planning, environmental monitoring, and terrain analysis. Although the task may seem simple at first glance, it encompasses a sequence of phases, each with its own technical complexity and decision points.

This article aims to explain the workflow used to produce a high-quality DTM from LiDAR data, detailing each processing step. We will also explore two alternative approaches to classify ground points: one based on a traditional method and one powered by Artificial Intelligence.

The screenshots shown are taken from Aplitop’s Tcp PointCloud Editor, but the procedures are applicable to any point cloud processing software.

1. General Workflow

The process of generating the DTM begins with the original LiDAR point cloud, a dense collection of three-dimensional points that are typically acquired through airborne laser scanning. The entire workflow can be summarized as follows:

2. Point Cloud Cleaning

Raw LiDAR data inevitably contains anomalies and inconsistencies due to sensor limitations, atmospheric conditions, or the nature of the scanned environment. Therefore, point cloud cleaning is the essential first step. This process can include tasks such as:

2.1 Removing High Noise Points

Outliers with extreme or implausible elevation values are removed using statistical or geometric filters. These can be caused by reflections from birds, airplanes, or missed signals.

2.2 Elimination of Isolated Points

Isolated points, those that have no close neighbors within a defined radius, are often indicative of noise or erroneous returns. Their removal ensures a cleaner, denser dataset with better interpolation behavior.

2.3 Filtering by Return Types

LiDAR pulses can have multiple returns, which are stored as additional point cloud attributes. If the main objective is to extract the ground, we can optionally discard the points that correspond to the first of many or intermediate.

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