GVLiDAR: an interactive web-based visualization framework to support geospatial measures on lidar data

Deibe, David; Amor, Margarita; Doallo, Ramón; Miranda, David; Cordero, Miguel

doi:10.1080/01431161.2016.1271476

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…Here, a simple algorithm, called SC-091-12 [35], is selected to perform this task. The reasons for this particular choice were (1) the consistency of output of the SC-091-12 is better than that of the Fusion [28], [36], (2) due to the development of DTM [26], and (3) its association with Spark and Cassandra, as the algorithm is programmed with Java which is a programming language promoted by Spark and Cassandra.…”

Section: A Geographical Dtm Processingmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

et al. 2020

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There is a need for improvement of tools to deal with large volumes of multimedia data effectively. In particular, real-time data processing is one of the major problems for multimedia data computing in remote sensing systems. Such big data systems have to offer effective management and computational efficiency for applications in real-time. In this paper, we propose a large-scale geological processing method for aerial Light Detection and Ranging (LiDAR) clouds containing multimedia data that ensures mobility and timeliness. By utilizing Spark and Cassandra, our proposed approach can significantly reduce the execution time of the time-consuming process. We investigate fast ground-only raster generation from huge LiDAR datasets. We observed that filtered cloud data ensuing from impartial consideration of neighboring zones could lead to classification errors on the boundaries. Therefore, an integrated approach is proposed to correct these errors to improve the classification consistency, achieve faster processing time, provide automatic error correction, obtain Digital Terrain Models (DTM), and minimize user intervention. These features can provide a framework for an on-demand DTM output and scalable application services. Furthermore, the proposed approach can expect to benefit other real-time applications in LiDAR systems.

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Section: A Geographical Dtm Processingmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

et al. 2020

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“…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]. Additionally, DTMs and DSMs can be used together with their source data for carrying out many different visual analyses or simply to compare the quality of different procedures and techniques employed for their creation [15].…”

Section: Introductionmentioning

confidence: 99%

Big Data Geospatial Processing for Massive Aerial LiDAR Datasets

2020

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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.

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Enhancing the Tourism Experience Using Mobile Augmented Reality: Geo-Visualization Techniques

Solanki,

Abbasi,

Hossain

et al. 2023

Lecture Notes in Networks and Systems

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GVLiDAR: an interactive web-based visualization framework to support geospatial measures on lidar data

Cited by 8 publications

References 19 publications

Notice of Violation of IEEE Publication Principles: Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

Notice of Violation of IEEE Publication Principles: Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

Big Data Geospatial Processing for Massive Aerial LiDAR Datasets

Enhancing the Tourism Experience Using Mobile Augmented Reality: Geo-Visualization Techniques

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