Improvement of Airborne LiDAR Intensity Image Content with Shaded nDSM and Assessment of Its Utility in Geospatial Data Generation

Author(s):  
B. Sadasiva Rao ◽  
G. Anil Kumar ◽  
C. Runjhun ◽  
C. V. K. V. P. Jagannadha Rao ◽  
G. Varaprasad Babu
2021 ◽  
Author(s):  
Raju Singh

<p>The data generation and collection of data have gone through a series of improvements over the past several years. Now, we observe that both aspects of data (generation and collection) have evolved, it creates another dimension – how to process the data at scale, and how to manage it.</p><p> </p><p>Relational DBMS has been a widely accepted idea behind processing and managing data, but it has its own pros and cons, the constraints on data to prevent integrity violation is seen as a trade-off between performance and management. With the advent in the storage, compute and network technology, we have reliably transited the state of relational database management. It’s not yet done. Handling exceptions have been very poor with a single point of failure with traditional DB architecture. However, with distributed systems, it only multiplies the failure points. Failure is expected, and hence the solution for availability is designed around these expected failures. Distributed computing adds functionalities such as performance, availability, and reliability.</p><p>But, that’s not all. We are living in an era, where we communicate very now and then, through different devices. Not only this, we generate, collect, manage data which are of variant types (mostly unstructured, multi-dimensional, carries lots of noise and bias, etc.). NoSQL DBMS, Apache Spark, and Hadoop come to rescue.</p><p> </p><p>One such area that exemplifies the use of big data is the transportation industry, which can encompass shipping, airline data, trucking, and the context we refer to cabs. NYC taxi data is available in an open-dataset that stores, among other things, geospatial data collected from individual taxis as they navigate the streets of New York City. Processing of geospatial data at this scale is very time-consuming and resource-intensive, as anyone who has used ArcGIS on a large dataset can attest. Distributed and parallel data processing presents an opportunity for faster processing of this type of data. The Apache Spark framework is ideal for this task as it is highly efficient with fast performance times. Additionally, it has libraries and APIs built in that allow it to process SQL queries, which many users are likely to be familiar with given its ubiquity.</p><p> </p><p>In the following report, we demonstrate our approaches to perform hot spot analysis on the NYC Taxi data. Hot-zone analysis performs range-join on the rectangle and point, to identify the boundaries from where most pickups happen. Hot-cell analysis uses statistical parameters to identify the zones by also considering time as an additional dimension.</p>


2017 ◽  
Author(s):  
Prof. Rajagopalan S ◽  
Yogalakshmi Jayabal

A vast amount of data is generated and collected every moment and often, data has a spatial and/or temporal aspect. This increasing data generation and collection is resulting in increasing volume and varying formats of data being collected and the geospatial data collection is no exception. This posses challenges in storing, processing, analyzing and visualizing the geospatial data. This paper discusses the big data paradigm of the geospatial data and presents a taxonomy for analysis of the geospatial data. The existing literature is studied and discussed based on the proposed taxonomy for analysis of geospatial data.


2016 ◽  
Vol 101 ◽  
pp. 217-226 ◽  
Author(s):  
Alexey Golubev ◽  
Ilya Chechetkin ◽  
Danila Parygin ◽  
Alexander Sokolov ◽  
Maxim Shcherbakov

2021 ◽  
Author(s):  
Raju Singh

<p>The data generation and collection of data have gone through a series of improvements over the past several years. Now, we observe that both aspects of data (generation and collection) have evolved, it creates another dimension – how to process the data at scale, and how to manage it.</p><p> </p><p>Relational DBMS has been a widely accepted idea behind processing and managing data, but it has its own pros and cons, the constraints on data to prevent integrity violation is seen as a trade-off between performance and management. With the advent in the storage, compute and network technology, we have reliably transited the state of relational database management. It’s not yet done. Handling exceptions have been very poor with a single point of failure with traditional DB architecture. However, with distributed systems, it only multiplies the failure points. Failure is expected, and hence the solution for availability is designed around these expected failures. Distributed computing adds functionalities such as performance, availability, and reliability.</p><p>But, that’s not all. We are living in an era, where we communicate very now and then, through different devices. Not only this, we generate, collect, manage data which are of variant types (mostly unstructured, multi-dimensional, carries lots of noise and bias, etc.). NoSQL DBMS, Apache Spark, and Hadoop come to rescue.</p><p> </p><p>One such area that exemplifies the use of big data is the transportation industry, which can encompass shipping, airline data, trucking, and the context we refer to cabs. NYC taxi data is available in an open-dataset that stores, among other things, geospatial data collected from individual taxis as they navigate the streets of New York City. Processing of geospatial data at this scale is very time-consuming and resource-intensive, as anyone who has used ArcGIS on a large dataset can attest. Distributed and parallel data processing presents an opportunity for faster processing of this type of data. The Apache Spark framework is ideal for this task as it is highly efficient with fast performance times. Additionally, it has libraries and APIs built in that allow it to process SQL queries, which many users are likely to be familiar with given its ubiquity.</p><p> </p><p>In the following report, we demonstrate our approaches to perform hot spot analysis on the NYC Taxi data. Hot-zone analysis performs range-join on the rectangle and point, to identify the boundaries from where most pickups happen. Hot-cell analysis uses statistical parameters to identify the zones by also considering time as an additional dimension.</p>


2015 ◽  
Vol 6 (1) ◽  
pp. 19-29 ◽  
Author(s):  
G. Bitelli ◽  
P. Conte ◽  
T. Csoknyai ◽  
E. Mandanici

The management of an urban context in a Smart City perspective requires the development of innovative projects, with new applications in multidisciplinary research areas. They can be related to many aspects of city life and urban management: fuel consumption monitoring, energy efficiency issues, environment, social organization, traffic, urban transformations, etc. Geomatics, the modern discipline of gathering, storing, processing, and delivering digital spatially referenced information, can play a fundamental role in many of these areas, providing new efficient and productive methods for a precise mapping of different phenomena by traditional cartographic representation or by new methods of data visualization and manipulation (e.g. three-dimensional modelling, data fusion, etc.). The technologies involved are based on airborne or satellite remote sensing (in visible, near infrared, thermal bands), laser scanning, digital photogrammetry, satellite positioning and, first of all, appropriate sensor integration (online or offline). The aim of this work is to present and analyse some new opportunities offered by Geomatics technologies for a Smart City management, with a specific interest towards the energy sector related to buildings. Reducing consumption and CO2 emissions is a primary objective to be pursued for a sustainable development and, in this direction, an accurate knowledge of energy consumptions and waste for heating of single houses, blocks or districts is needed. A synoptic information regarding a city or a portion of a city can be acquired through sensors on board of airplanes or satellite platforms, operating in the thermal band. A problem to be investigated at the scale A problem to be investigated at the scale of the whole urban context is the Urban Heat Island (UHI), a phenomenon known and studied in the last decades. UHI is related not only to sensible heat released by anthropic activities, but also to land use variations and evapotranspiration reduction. The availability of thermal satellite sensors is fundamental to carry out multi-temporal studies in order to evaluate the dynamic behaviour of the UHI for a city. Working with a greater detail, districts or single buildings can be analysed by specifically designed airborne surveys. The activity has been recently carried out in the EnergyCity project, developed in the framework of the Central Europe programme established by UE. As demonstrated by the project, such data can be successfully integrated in a GIS storing all relevant data about buildings and energy supply, in order to create a powerful geospatial database for a Decision Support System assisting to reduce energy losses and CO2 emissions. Today, aerial thermal mapping could be furthermore integrated by terrestrial 3D surveys realized with Mobile Mapping Systems through multisensor platforms comprising thermal camera/s, laser scanning, GPS, inertial systems, etc. In this way the product can be a true 3D thermal model with good geometric properties, enlarging the possibilities in respect to conventional qualitative 2D images with simple colour palettes. Finally, some applications in the energy sector could benefit from the availability of a true 3D City Model, where the buildings are carefully described through three-dimensional elements. The processing of airborne LiDAR datasets for automated and semi-automated extraction of 3D buildings can provide such new generation of 3D city models.


2008 ◽  
Vol 67 (19) ◽  
pp. 1777-1790 ◽  
Author(s):  
C. Cruz-Ramos ◽  
R. Reyes-Reyes ◽  
J. Mendoza-Noriega ◽  
Mariko Nakano-Miyatake ◽  
Hector Manuel Perez-Meana

Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.


2015 ◽  
Vol 4 (1) ◽  
pp. 1224-1228 ◽  
Author(s):  
Debasish Chakraborty ◽  
◽  
Debanjan Sarkar ◽  
Shubham Agarwal ◽  
Dibyendu Dutta ◽  
...  

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