Géolocation recently applied for a grant through the Natural Sciences and Engineering Research Council of Canada (NSERC) Partner Engagement Program to support the development of its automated point clouds processing tools in LiDAR. The development of these tools is intended to respond the growing demand of private clients, provincial and federal departments and agencies related to the computation of laser surveys in various fields like energy, transportation, urban planning and forestry.
As the volume of production is always increasing and distributed on shorter periods, our company aims to optimize its processing and limit the visual analysis by developing algorithms that isolate and classify automatically elements from a points cloud. The purpose of this approach is to increase productivity by creating innovative and tailor-made tools by reducing the time allowed to manual interventions while maintaining our highest standards of quality. The democratization of LiDAR technologies and the globalization of the market make inevitable the perpetual development of made-in-house tools to minimize the production time and thus to distinguish itself from the competition. That’s why every year, Géolocation invests in research and development to improve its laser data processing techniques using multiple specialized softwares. However, few operations still require a visual analysis and validation.
Thanks to the Engage program, Géolocation has therefore requested the collaboration of professor Mir Abolfazl Mostafavi and his team of the Department of Geomatic Sciences of Laval University and Réseau Convergence to enable or improve the automatic detection of elements from points clouds, for example : hydrographic networks, bridges, trees (individual), power lines, street lights, signs, etc. The support of external resources and researchers to this development will certainly help us meet our objectives. Géolocation already actively involves its staff in design and development to enable skills transfer, participate in collective decision-making and have a good understanding of the classification tools that will be established.
The use of ever more efficient systems increases the acquisition density and efficiency by minimizing the number of flight lines. Over the years, the LiDAR coverage of the territory continues to grow, thus multiplying the information to extract from point clouds. Géolocation wishes to contribute to the collection of this great amount of information and to help you discovering it.