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

• Applicant: Professor Dr. Bernhard Höfle
• Subject Area: Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 496418931

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. (1) A key objective is to 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. (2) Furthermore, we would like to 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. (3) A new concept of ‘dynamic objects’ in VLS simulations will be developed and tested, which enables e.g. to include vegetation with phenological changes and also moving objects (e.g. plants, cars, people). 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.

• DFG Programme: Research Grants