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

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.

Graph Attention Feature Fusion Network for ALS Point Cloud Classification

“…Airborne laser scanning (ALS), also known as airborne light detection and ranging (LiDAR), is an important active remote sensing technique that has displayed rapid development in recent years [ 1 ]. The technique has the advantage of quickly acquiring large-scale, high-density, and high-precision 3D ground data, and plays an increasingly important role in many applications, including topographic mapping [ 2 ], urban planning [ 3 ], forest biomass estimation [ 4 ], environmental monitoring [ 5 ], and power line detection [ 6 ]. By employing ALS for ground-based scanning, a massive and disordered point cloud can be obtained.…”