Extracting Object Heights From LiDAR & Aerial Imagery

TL;DR

This paper presents a procedural method for extracting object heights from LiDAR and aerial imagery, comparing traditional techniques with emerging AI-driven approaches, emphasizing practical applications for engineers.

Contribution

It introduces a procedural methodology for height extraction and discusses the integration of newer AI methods, including language models, for spatial data analysis.

Findings

Procedural methods enable height extraction without deep learning.

State-of-the-art AI approaches incorporate point cloud, imagery, and text encoding.

Traditional methods remain relevant alongside emerging generative AI techniques.

Abstract

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.