Automatic Parametrization of Urban Areas Using ALS Data: The Case Study of Santiago de Compostela

Author:

Soilán Mario,Riveiro BelénORCID,Liñares Patricia,Pérez-Rivas Andrea

Abstract

Nowadays, gathering accurate and meaningful information about the urban environment with the maximum efficiency in terms of cost and time has become more relevant for city administrations, as this information is essential if the sustainability or the resilience of the urban structure has to be improved. This work presents a methodology for the automatic parametrization and characterization of different urban typologies, for the specific case study of Santiago de Compostela (Spain), using data from Aerial Laser Scanners (ALS). This methodology consists of a number of sequential processes of point cloud data, using exclusively their geometric coordinates. Three of the main elements of the urban structure are assessed in this work: intersections, building blocks, and streets. Different geometric and contextual metrics are automatically extracted for each of the elements, defining the urban typology of the studied area. The accuracy of the measurements is validated against a manual reference, obtaining average errors of less than 3%, proving that the input data is valid for this assessment.

Funder

Ministerio de Economía, Industria y Competitividad, Gobierno de España

Xunta de Galicia

Publisher

MDPI AG

Subject

Earth and Planetary Sciences (miscellaneous),Computers in Earth Sciences,Geography, Planning and Development

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Building Instance Mapping From ALS Point Clouds Aided by Polygonal Maps;IEEE Transactions on Geoscience and Remote Sensing;2022

2. A Gap-Based Method for LiDAR Point Cloud Division;IEEE Geoscience and Remote Sensing Letters;2022

3. Graph Attention Feature Fusion Network for ALS Point Cloud Classification;Sensors;2021-09-15

4. Façade Separation in Ground-Based LiDAR Point Clouds Based on Edges and Windows;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2019-03

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