scholarly journals RSDB: an easy to deploy open‐source web platform for remote sensing raster and point cloud data management, exploration and processing

Ecography ◽  
2020 ◽  
Author(s):  
Wöllauer Stephan ◽  
Zeuss Dirk ◽  
Magdon Paul ◽  
Nauss Thomas
Keyword(s):  
Remote Sensing ◽  
Data Management ◽  
Open Source ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Cloud Data Management ◽  
Web Platform

scholarly journals Model of Point Cloud Data Management System in Big Data Paradigm

2018 ◽  
Vol 7 (7) ◽  
pp. 265 ◽  
Author(s):  
Vladimir Pajić ◽  
Miro Govedarica ◽  
Mladen Amović
Keyword(s):  
Big Data ◽  
Data Management ◽  
Management System ◽  
Point Cloud ◽  
Data Management System ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Cloud Data Management

scholarly journals Benchmarking and improving point cloud data management in MonetDB

2015 ◽  
Vol 6 (2) ◽  
pp. 11-18 ◽  
Author(s):  
Oscar Martinez-Rubi ◽  
Peter van Oosterom ◽  
Romulo Gonçalves ◽  
Theo Tijssen ◽  
Milena Ivanova ◽  
...  
Keyword(s):  
Data Management ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Cloud Data Management

Realistic Benchmarks for Point Cloud Data Management Systems

2016 ◽  
pp. 1-30 ◽  
Author(s):  
Peter van Oosterom ◽  
Oscar Martinez-Rubi ◽  
Theo Tijssen ◽  
Romulo Gonçalves
Keyword(s):  
Data Management ◽  
Point Cloud ◽  
Management Systems ◽  
Point Cloud Data ◽  
Data Management Systems ◽  
Cloud Data ◽  
Cloud Data Management

A Hybrid Spatial Index for Massive Point Cloud Data Management and Visualization

2014 ◽  
Vol 18 ◽  
pp. 97-108
Author(s):  
Jiansi Yang ◽  
Xianfeng Huang
Keyword(s):  
Data Management ◽  
Point Cloud ◽  
Spatial Index ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Massive Point ◽  
Cloud Data Management

Massive point cloud data management: Design, implementation and execution of a point cloud benchmark

2015 ◽  
Vol 49 ◽  
pp. 92-125 ◽  
Author(s):  
Peter van Oosterom ◽  
Oscar Martinez-Rubi ◽  
Milena Ivanova ◽  
Mike Horhammer ◽  
Daniel Geringer ◽  
...  
Keyword(s):  
Data Management ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Massive Point ◽  
Cloud Data Management

Dictionary Compression in Point Cloud Data Management

2019 ◽  
Vol 5 (1) ◽  
pp. 1-25
Author(s):  
Mirjana Pavlovic ◽  
Kai-Niklas Bastian ◽  
Hinnerk Gildhoff ◽  
Anastasia Ailamaki
Keyword(s):  
Data Management ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Cloud Data Management

scholarly journals Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

2019 ◽  
Vol 12 (1) ◽  
pp. 50 ◽  
Author(s):  
Mahyar Aboutalebi ◽  
Alfonso F. Torres-Rua ◽  
Mac McKee ◽  
William P. Kustas ◽  
Hector Nieto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Point Cloud ◽  
Spatial Information ◽  
Point Cloud Data ◽  
Area Index ◽  
Cloud Data ◽  
Fractional Cover ◽  
Spatially Distributed

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

Unlocking modern nation-scale LiDAR datasets with FOSS – the Laserchicken framework

2020 ◽  
Author(s):  
Meiert W. Grootes ◽  
Christiaan Meijer ◽  
Zsofia Koma ◽  
Bouwe Andela ◽  
Elena Ranguelova ◽  
...  
Keyword(s):  
High Resolution ◽  
Open Source ◽  
Point Cloud ◽  
Laser Scanning ◽  
Vegetation Type ◽  
Ecosystem Structure ◽  
Point Cloud Data ◽  
Ecosystem Research ◽  
Cloud Data ◽  
Wide Range

<p>LiDAR as a remote sensing technology, enabling the rapid 3D characterization of an area from an air- or spaceborne platform, has become a mainstream tool in the (bio)geosciences and related disciplines. For instance, LiDAR-derived metrics are used for characterizing vegetation type, structure, and prevalence and are widely employed across ecosystem research, forestry, and ecology/biology. Furthermore, these types of metrics are key candidates in the quest for Essential Biodiversity Variables (EBVs) suited to quantifying habitat structure, reflecting the importance of this property in assessing and monitoring the biodiversity of flora and fauna, and consequently in informing policy to safeguard it in the light of climate change an human impact.</p><p>In all these use cases, the power of LiDAR point cloud datasets resides in the information encoded within the spatial distribution of LiDAR returns, which can be extracted by calculating domain-specific statistical/ensemble properties of well-defined subsets of points.  </p><p>Facilitated by technological advances, the volume of point cloud data sets provided by LiDAR has steadily increased, with modern airborne laser scanning surveys now providing high-resolution, (super-)national scale datasets, tens to hundreds of terabytes in size and encompassing hundreds of billions of individual points, many of which are available as open data.</p><p>Representing a trove of data and, for the first time, enabling the study of ecosystem structure at meter resolution over the extent of tens to hundreds of kilometers, these datasets represent highly valuable new resources. However, their scientific exploitation is hindered by the scarcity of Free Open Source Software (FOSS) tools capable of handling the challenges of accessing, processing, and extracting meaningful information from massive multi-terabyte datasets, as well as by the domain-specificity of any existing tools.</p><p>Here we present Laserchicken a FOSS, user-extendable, cross-platform Python tool for extracting user-defined statistical properties of flexibly defined subsets of point cloud data, aimed at enabling efficient, scalable, and distributed processing of multi-terabyte datasets. Laserchicken can be seamlessly employed on computing architectures ranging from desktop systems to distributed clusters, and supports standard point cloud and geo-data formats (LAS/LAZ, PLY, GeoTIFF, etc.) making it compatible with a wide range of (FOSS) tools for geoscience.</p><p>The Laserchicken feature extraction tool is complemented by a FOSS Python processing pipeline tailored to the scientific exploitation of massive nation-scale point cloud datasets, together forming the Laserchicken framework.</p><p>The ability of the Laserchicken framework to unlock nation-scale LiDAR point cloud datasets is demonstrated on the basis of its use in the eEcoLiDAR project, a collaborative project between the University of Amsterdam and the Netherlands eScience Center. Within the eEcoLiDAR project, Laserchicken has been instrumental in defining classification methods for wetland habitats, as well as in facilitating the use of high-resolution vegetation structure metrics in modelling species distributions at national scales, with preliminary results highlighting the importance of including this information.</p><p>The Laserchicken Framework rests on FOSS, including the GDAL and PDAL libraries as well as numerous packages hosted on the open source Python Package Index (PyPI), and is itself also available as FOSS (https://pypi.org/project/laserchicken/ and https://github.com/eEcoLiDAR/ ).</p>

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