LiDAR Remote Sensing and Applications

The aim of this module is to deepen the understanding of Laser Remote Sensing techniques. The module provides an overview of today’s common sensors used for the Light Detection And Ranging (LiDAR) Remote Sensing along with the practical use case scenarios revolving around use of Point Cloud data and its handling for feature extraction. Both aspects, theory and practice will provide a good ground for standalone handling of the collected LiDAR data and further development based on the common elements (data fusion, cross platform adaptation). The module begins with a basic overview of the sensory platform and theoretical background on LiDAR. This will be followed by an in depth analysis of specific research applications (Satellite, airborne, terrestrial and bathymetric). Practical lessons will cater the need for hands on analysis of LiDAR obtained Point Cloud data. The initial lessons will provide basic principles of loading the data and exploration of the existing features. This will be upgraded with practical analysis of pre-classified point cloud data with derivation of DEM models and basic feature extraction based on class coding. Final part of the practical work will focus on advanced aspects of working with non-classified point cloud data in order to produce DEM models and extract specific features (vegetation masking, building extraction). This course will also use practical elements obtained from the Object-Based analysis (OBIA) methodology domain. A special focus will be given to the topic of storing and using LiDAR data with databases.

1,5 months



The module is free of charge for UNIGIS students working to meet their elective subject requirements. Included are:

  • all related study materials
  • supervision and assessment
  • module accreditation according to the curriculum

ClubUNIGIS members can register at a price of € 150,-. Included are:

  • all related study materials
  • supervision and assessment
  • course certificate upon completion of the module

  • Understanding the technology behind LiDAR as an active sensor and what makes it different from the other existing Remote Sensing approaches
  • Developing thorough understanding of the complex process from collecting the LiDAR data to generation of the final modeled outputs
  • Gaining an insight into the structure of a Point Cloud and obtaining hands-on experience in dealing with such data

Core UNIGIS modules 1-3 or equivalent understanding of GIS, data modeling, data structures and data acquisition. If you are not sure whether you qualify, please contact (UNIGIS DE) or (UNIGIS INT).

We would like to inform you that this is an exclusively english language module, hence any kind of communication with the module lecturer should be in English. A discussion forum is maintained in moodle in order to support efficient module instruction. You are requested to submit all your questions related to this module to this forum only. The lecturer will check all incoming comments on a regular basis. He will answer your questions or provide you with pointers for solving your problems. The module is delivered in form of an instructed self-study that is based on explorative learning process and process. Theoretical concepts are complemented with practice oriented examples demonstrated with help of multimedia elements. Upon completion of the module students are requested to evaluate the module, which is a part of our quality assurance policy and practice.

Trimbles eCognition Developer, FugroViewer

Students are supposed to work with the software eCognition Developer Trial 64-bit. Please be aware that eCognition developer Trial will not work with Windows 32-bit or “Home” Versions! If you need a Windows 7 Professional 64-bit EDU copy, please ask for a Dreamspark Account to get one!
Software Requirements
– Microsoft Windows 7 Professional (64-Bit)
– Microsoft Windows 8.1 (64-Bit)
Minimum Hardware Requirements
– Intel Pentium 4 or compatible / Intel Dual Core or compatible
– NVIDIA or ATI OpenGL graphics card
– 1 GB RAM l 50 GB available hard disk space
– 1280 x 1024 display.
Recommended Hardware Requirements
– Intel Dual Core or Quad Core
– NVIDIA or ATI OpenGL graphics card
– 4 GB RAM 1 l 200 GB available hard disk space
– 1600 x 1200 display

The assessment is based on your completed assignments. They must be submitted in written format (.PDF/.DOC) to the Dropbox within the required time period. If assignments are submitted late, the lecturer is not obligated to grade them. It will be listed as such on your transcript.

Lesson 1 – What is LiDAR and why do we need it?
This lesson provides an introduction to creating and editing spatial data. This includes creating new vector layers, selecting features, and taking measurements. Furthermore, this lesson covers editing feature geometries and attributes, layer reprojections, file format conversions, and data joins.

Lesson 2 – What is LiDAR and why do we need it? (Second Part)
This lesson provides an introduction to a very specific element of LiDAR Remote Sensing: multiple returns and its use in LiDAR Remote Sensing. We begin with exploration of different, other, ways to count the returns coming from the environment. We will explain and understand the behaviour from various material as land covers when it comes to returns and intensity. Apart from this, we take a beginners look at point clouds. What they are and how do we form them. The last part is used as an introduction to the Lesson 3 where the whole philosophy of Point Clouds will be presented in details.

Lesson 3 – Point Clouds! What can we do with them? 
This lesson provides a deeper look into Point Clouds and their use in LiDAR Remote Sensing. We continue with exploration of different, other, ways to store the point cloud data (database) and we define methods for data extraction and modelling. We mention simple procedures necessary to convert point cloud data into .las format from other possible data formats. Apart from this, we take a deeper look into ways on how to handle Point Cloud data (feature extraction, data modelling, practical use case scenarios). We also show one practical use case scenario for data extraction and Point Cloud modelling done at the Faculty of Geodesy, University of Zagreb.

Lesson 4 – Let’s fly, space based LiDAR missions.
There is many satellites out there that contain LiDAR systems on board. Some are already active, some are lost, some are in the process of being planned. We will within this lecture enumerate those systems and provide some background data on the mission and achieved goals.

Lesson 5 – Still flying high, Airborne LiDAR. 
After Satellite-borne systems, Air-borne LiDAR would be the most interesting part with wide applications. In this lesson we will go through a short history of airborne systems from back in the 1970´s all the way to today. We will see how the data is used and what are the advantages of these systems when compared to other spatial approaches like photogrammetry.

Lesson 6 – Let’s get grounded, Terrestrial LiDAR.
Terrestrial LiDAR systems, along with the airborne ones, are two of the oldest use cases of this technology. This lecture will provides us with a thorough overview of existing terrestrial scanning systems along with the old ones. We wish to paint the picture of the progress through years and we will end it by showing the newest “state-of-the-art” products.

Lesson 7 – Let’s dive… Bathymetric LiDAR.
We will also take a look at the ongoing initiatives that allow us to freely download airborne LiDAR data. Some of the countries like UK and Netherlands will soon be releasing their LiDAR data to general scientific community.