Universitätssiegel

Funding
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
Project number: 496418931

BMBF

Duration
2022 - 2025

 

Contact
Prof. Bernhard Höfle
Institute of Geography, Heidelberg University, Germany

 

VirtuaLearn3D - Virtual Laser Scanning for Machine Learning Algorithms in Geographic 3D Point Cloud Analysis

This website can be accessd via a short URL: www.uni-heidelberg.de/virtualearn3d

News

All research news can be found on ResearchGate.

Objective

Topographic laser scanning (LS) is a leading remote sensing technique to derive detailed 3D point cloud representations of the Earth’s surface and its objects. Virtual laser scanning (VLS) simulations recreate real-world scenarios of LS acquisitions in a computer environment. VLS is useful when real experiments are not feasible, e.g. due to technical, economic and logistic constraints. Recent advances in machine learning, in particular supervised deep learning, indicate a huge potential to improve geographic 3D point cloud analysis of complex natural objects (e.g. vegetation) and scenes (e.g. geomorphological settings). The success of deep learning algorithms strongly depends on the availability of high-quality and appropriately large amounts of training data. The main aim of this project is to advance the concept of virtual laser scanning to tackle the lack of training data to enable powerful machine learning algorithms for geographic point cloud analysis.

This proposed methodological step will push large-scale usage of VLS simulations for machine learning and opens up completely new fields of applications. This project will focus on airborne laser scanning (incl. UAV-borne LS) and the tasks of object-based tree species classification and semantic urban scene classification, though the relevance of the developed generic concepts is not limited by the investigated examples.

VL3D

Coupling of learning algorithms with virtual laser scanning and real point cloud data.

This project will address the following central objectives:

  • Find effective combinations of real LS data with theoretically unlimited amounts of simulated LS data for supervised training. Effective solutions can close the reality gap from simulated to real data and keep high classification accuracy while reducing costly input data.
  • Find out to what degree VLS data generation can support transfer learning strategies to enable the usage of pre-trained models for transfer to different geographic characteristics and types of LS data. VLS-supported transfer learning is highly demanded due to drastically increasing availability of LiDAR technology in sciences and also on daily-life devices.
  • Develop and test a new concept of ‘dynamic objects’ in VLS simulations, which enables e.g. to include vegetation with phenological changes and also moving objects (e.g. plants, cars, people).

VirtuaLearn3D will thereby push large-scale usage of VLS simulations for machine learning - in particular deep learning approaches - and our results will open up new fields of applications in science and industry.

Related Projects
  • HELIOS++: Heidelberg LiDAR Operations Simulator.
  • SYSSIFOSS: Synthetic structural remote sensing data for improved forest inventory models.
Related Publications
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