Big Data Geospatial Processing for Massive Aerial LiDAR Datasets

David Deibe,Margarita Amor,Ramón Doallo

doi:10.3390/rs12040719

David Deibe, Margarita Amor + Show 1 more

Open Access

https://doi.org/10.3390/rs12040719

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Journal: Remote Sensing	Publication Date: Feb 21, 2020
Citations: 14	License type: CC BY 4.0

Affiliation: University of A Coruña

Abstract

For years, Light Detection and Ranging (LiDAR) technology has been considered as a challenge when it comes to developing efficient software to handle the extremely large volumes of data this surveying method is able to collect. In contexts such as this, big data technologies have been providing powerful solutions for distributed storage and computing. In this work, a big data approach on geospatial processing for massive aerial LiDAR point clouds is presented. By using Cassandra and Spark, our proposal is intended to support the execution of any kind of heavy time-consuming process; nonetheless, as an initial case of study, we have focused on fast ground-only rasters obtention to generate digital terrain models (DTMs) from massive LiDAR datasets. Filtered clouds obtained from the isolated processing of adjacent zones may exhibit errors located on the boundaries of the zones in the form of misclassified points. Usually, this type of error is corrected through manual or semi-automatic procedures. In this work, we also present an automated strategy for correcting errors of this type, improving the quality of the classification process and the DTMs obtained while minimizing user intervention. The autonomous nature of all computing stages, along with the low processing times achieved, opens the possibility of considering the system as a highly scalable service-oriented solution for on-demand DTM generation or any other geospatial process. Said solution would be a highly useful and unique service for many users in the LiDAR field, and one which could get near to real-time processing with appropriate computational resources.

Highlights

Nowadays, Light Detection and Ranging (LiDAR) technology stands out as one of the most valuable sources of geospatial information, providing enormous benefits in a wide range of scientific and professional fields, such as agroforestry [1] or land monitoring [2], among many others
LiDAR is considered as a challenge when it comes to developing efficient proposals to handle the extremely large volumes of data this surveying method is able to collect; researchers are forced to constantly look for new approaches and solutions in order to overcome all constraints related to the manipulation of such volumes of information [3,4]
Several academic publications have already discussed the advantages of following a big data approach in geospatial information contexts [6,7,8,9], some of which discuss the benefits of using big data in the specific field of LiDAR [10,11,12]

Summary

Introduction

LiDAR technology stands out as one of the most valuable sources of geospatial information, providing enormous benefits in a wide range of scientific and professional fields, such as agroforestry [1] or land monitoring [2], among many others. Several academic publications have already discussed the advantages of following a big data approach in geospatial information contexts [6,7,8,9], some of which discuss the benefits of using big data in the specific field of LiDAR [10,11,12]. GIS (Geographic Information Science) elevation models, such as DSMs (Digital Surface Models) or DTMs (Digital Terrain Models), are some of the most important and valuable products derived from LiDAR point clouds, as these raster-type three-dimensional (3D) models are the core element in many geospatial processes, e.g., biomass estimation [13] or linear feature extraction [14]. DTMs and DSMs can be used together with their source data for carrying out many different visual analyses or to compare the quality of different procedures and techniques employed for their creation [15]

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