Extracting Object Heights From LiDAR & Aerial Imagery
This provides a practical tool for engineers working with geospatial data, though it appears incremental as it builds on existing segmentation methods.
The paper presents a procedural method for extracting object heights from LiDAR and aerial imagery, leveraging state-of-the-art object segmentation to enable height extraction without deep learning expertise, while also discussing newer generative AI approaches.
This work shows a procedural method for extracting object heights from LiDAR and aerial imagery. We discuss how to get heights and the future of LiDAR and imagery processing. SOTA object segmentation allows us to take get object heights with no deep learning background. Engineers will be keeping track of world data across generations and reprocessing them. They will be using older procedural methods like this paper and newer ones discussed here. SOTA methods are going beyond analysis and into generative AI. We cover both a procedural methodology and the newer ones performed with language models. These include point cloud, imagery and text encoding allowing for spatially aware AI.