# A Method Considering Multi-Dimensional Feature Differences for Extracting Rural Buildings Based on Airborne LiDAR

## TL;DR

This paper introduces a new framework for accurately identifying rural buildings from LiDAR data by using multi-dimensional features and reducing computational redundancy.

## Contribution

The novel contribution is a spatial hierarchical framework that dynamically segments terrain and uses geometric and morphological features for precise rural building classification.

## Key findings

- The framework achieved over 93.37% precision and 97.05% recall in building classification from airborne LiDAR data.
- It effectively handles rural areas where buildings and vegetation are of similar height and interwoven.
- The method reduces redundant computations by focusing on relevant regions of interest.

## Abstract

What are the main findings?
A framework comprising ground point classification, building Region of Interest filtering, and refined extraction is proposed based on airborne LiDAR data for building classification purposes in complex rural scenes.Ground points form the foundation for building classifications. Region of Interest filtering based on geometric features further confirms building scopes, then a set of morphological features primarily based on local dimensionality models enables precise building classification.

A framework comprising ground point classification, building Region of Interest filtering, and refined extraction is proposed based on airborne LiDAR data for building classification purposes in complex rural scenes.

Ground points form the foundation for building classifications. Region of Interest filtering based on geometric features further confirms building scopes, then a set of morphological features primarily based on local dimensionality models enables precise building classification.

What are the implications of the main findings?
The proposed framework employs a spatial hierarchical strategy to extract building data, thereby avoiding the substantial redundant computations caused by traversing the entire point cloud. By precisely capturing local features of point clouds within an optimal neighborhood range, it achieves high-precision building classification.This work provides a comprehensive approach for point cloud recognition of low-rise structures in rural areas, particularly suited for regions where vegetation and buildings are of similar height and exhibit an interlocking pattern. It demonstrates significant potential for use with other intelligent building classification methods.

The proposed framework employs a spatial hierarchical strategy to extract building data, thereby avoiding the substantial redundant computations caused by traversing the entire point cloud. By precisely capturing local features of point clouds within an optimal neighborhood range, it achieves high-precision building classification.

This work provides a comprehensive approach for point cloud recognition of low-rise structures in rural areas, particularly suited for regions where vegetation and buildings are of similar height and exhibit an interlocking pattern. It demonstrates significant potential for use with other intelligent building classification methods.

Research on extracting building from airborne point clouds is abundant, yet discussions regarding scenarios where vegetation and building structures are closely intertwined with similar height in rural areas remain relatively scarce. This thesis adopts a region representative of typical rural building features in China as an experimental site to conduct research on building classification procedures from airborne point clouds. Firstly, the multi-level grid size is dynamically determined through slope analysis to creatively segment and recognize terrain type, then differentiated filtering parameters are applied to various terrains to fully extract ground points, providing a ground reference for building classification. Secondly, the selection of building Region of Interest is conducted by multiple geometric feature differences between building and other objects based on watershed segmentation results, which eliminates interference from non-building points, significantly reducing redundant and unnecessary mathematical computation. Finally, refined building classification is achieved based on multiple morphological differences between buildings and other objects. The experimental results show that the precision, recall, and F1 of both datasets exceeded 93.37%, 97.05%, and 95.17%, respectively. The average precision, recall, and F1 reached 94.02%, 97.20%, and 95.58%, respectively. This method demonstrates successful building classification in rural areas, showing strong adaptability and practicality for the extraction of various building data.