This paper addresses the problem of detecting humans in a point cloud taken with a 3D-LiDAR onboard a UAV. The potential use cases of this technology are numerous, examples include security and surveillance, disaster relief and search and rescue operations. In this paper, a CNN-based approach is proposed which is able to analyse point clouds returned by a 3D LiDAR sensor in such a way that it can detect humans. The algorithm described here consists of 3 main components: data pre-processing, post-processing, and human classification. In this paper objects were assigned to one of two classes: human and non-human. The classification was performed by projecting the 3D point cloud onto a series of 2D planes using occupancy grid mapping. This creates a set of silhouettes of the object corresponding to the top, front and side views. Classification is achieved by supervised CNNs using single-view and multi-view (3 channels) images patches. Raw DataRaw data from airborne LiDAR Data Synch. LOAM-based LiDAR Point-Cloud synchronization Ground detection RANSAC to determine the ground plane Data Alignment Align Point-cloud w/ reference coordinate axes Define ROI Use reference point to define rectangular ROI Clustering Separate Pointcloud into clusters Occ. Grid Project the Points onto 2D Grid map Process Image Convert grid to 2D image at cluster location Human classification CNN-based classifier Point cloud pre-processing Post-processing CNN-based classification

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