Breaking the boundaries: The Integration of GIS, AI, and LiDAR for Digital Innovation.

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Processing very large lidar point clouds is slow and expensive. With MATLAB​,​ Spacesium developed a deep learning solution that can label lidar point clouds with better accuracy and ​increased ​speed.

Overcoming Challenges in Integrating GIS, AI, and LiDAR for Enhanced Spatial Intelligence

A key component of the digital transformation of GIS data is the rapid integration and interrogation of large datasets, LiDAR, and visual information to support rapid decision making.  For example, Smart city automation relies on accurate knowledge of street infrastructure, such as the location and condition of utility poles and power lines, while forestry managers require canopy and trunk estimation and digital terrain mapping to enhance their operations. Yet, despite the promise significant challenges remain, including the need for rapid but accurate processing of spatial point clouds and the integration of visual and multispectral data with LiDAR to provide information beyond a simple (x,y,z) location.

Traditional feature-based methods for processing point cloud data can be slow and imprecise. In some cases, such as on mine sites, feature-based algorithms may misinterpret ground profiles as buildings due to the planar shapes of spoil heaps, leading to inaccuracies.  In contrast, at Spacesium, we are using cutting edge Deep Artificial Intelligence algorithms, such as R-CNN, Deeplab v3 and PointNet++, in conjunction with semi-supervised retraining, to rapidly segment and classify point clouds utilising cloud scale resources.

Optimizing Fusion of Visual and Multispectral Data for Enhanced Spatial Analytics and Autonomous Navigation

The integration of visual and multispectral data presents a unique set of challenges and opportunities. While it allows for more than just the specification of the position of an XYZ point cloud, using both location and visual data can enable the determination of viable routes for autonomous vehicles.  On the challenge side, it is computationally expensive computing fusing these data sets that are captured in separate imaging systems.  At Spacesium, we have implemented custom algorithms based on tools from the Image Processing Toolbox and the Computer Vision Toolbox to efficiently correct, compute and fuse these data sets.

Read about how [Spacesium created a deep learning system to segment large LiDAR point clouds with MATLAB].