Geospatial services

Modelling of waste water pipe network by using Hand held Lidar scanner point cloud data

SBL was recently awarded a waste water pipe modelling project from a handheld scanner point cloud data by a global firm of 3D scanner manufacturers and retailers from the UK.


Figure 1- Input point cloud data (Isometric View)

A section of sewer pipe network and its connections (refer Figure 1 above) was modelled with the help of point cloud data from a hand-held scanner. A major challenge in the project was that the input point cloud data was provided without RGB values since the LiDAR survey was carried inside a pipe where there is no light.
SBL carried out the detailed mapping from the LiDAR point cloud data using “as is”, rule without generalizing or offsetting any feature. Cross section views were used to capture the data to the highest possible accuracy wherever changes occur in terms of shape and size.
Check out the images below which depict graphically the level of expertise and care that was taken by the team at SBL to execute the project.


Figure 2- Input point cloud and extracted pipes (Isometric View)


Figure 3 -Waste water Pipe network Surface model view


Figure 4 – Inside view of the waste water pipe


Figure 5 – Aspect angle view

This project marks another achievement by SBL in the LiDAR modelling domain. We are expecting to showcase few more such projects in our upcoming blogs.

BIM and LiDAR Team – SBL Knowledge Services Ltd.

Modelling of Road Assets using mobile point cloud data

Another feather in SBL’s cap. SBL was recently awarded a road asset mapping project from mobile point cloud data by a reputed firm of chartered land surveyors in the UK. The project area was mapping a stretch of the A937 highway passing through the town of Laurencekirk, Scotland, UK (see image below).

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Figure 1 – Project extents shown by highlighted blue and green lines

The length of road that was mapped was 2.3 kms and all road furniture (top and bottom of curbs, road edges, hedges and gates and walls etc.) markings (parking marks, bus stop marks etc) was captured to a high degree of accuracy. A major challenge in the project was that the input point cloud data was provided without RGB values since the mobile LiDAR survey was carried out at night.
SBL carried out the detailed mapping from the mobile point cloud data using “as is”, rule without generalizing or offsetting any feature. Cross section views at distances of less than 7m were used to capture the data to the highest possible accuracy. Check out the images below which depict graphically the level of expertise and care that was taken by the team at SBL to execute the project.

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Figure 2- Input point cloud data (Isometric View)

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Figure 3 – Profile view of the curb drawn with vertexes

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Figure 4 – Digitized top and bottom of curbs

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Figure 5 – Modelling of street markings

A significant achievement on this project was that SBL provided a “first time right” work with high levels of accuracy in a very short turnaround time of 4 working days. This was possible only because of the dedication and expertise of the LiDAR modelling team of SBL. We hope to come up with more such success stories in the coming days, so stay tuned.

Creating a 3D Model of a warehouse using point cloud data

SBL has been making steady and sustained progress in developing its BIM services portfolio over the last couple of years. Our last blog post on our BIM services was nearly 4 months back (October 2016) when we showcased our capabilities to model railway assets using point cloud data acquired from mobile and terrestrial scanners. Since then our BIM and LiDAR teams have been quite busy in working on a variety of BIM and LiDAR projects and pilot samples. One such interesting work was from the UK where our BIM team was given a 3D point cloud data acquired using ZEB-REVO handheld scanner. The ZEB-REVO handheld scanner was used to acquire the internal details of a warehouse at nearly 37000 ppm². The acquired point cloud data had only elevation values and no RGB values. No reference photographs were provided.

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Figure 1 – Input point cloud data thematically represented by elevation data

The lack of RGB values in the input point cloud data and the absence of reference photographs poses a challenge to anyone desiring to model the individual features present in the data. SBL overcame this challenge due to the availability of highly skilled and experienced modellers who were fully conversant of the features expected to be found in a warehouse. The internal features of the warehouse was modelled with high accuracy using Aecosim Building designer software from Bentley. The 3D model of the warehouse was rendered to create realistic looking and highly accurate photo images of the warehouse. Multiple views of the rendered model are included below.

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Figure 2- Rendered output of the warehouse (view 1)

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Figure 3 – Rendered output of the warehouse (view 2)

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Figure 4 – Rendered output of the warehouse (view 3)

This 3D models was used by the architects of the warehouse to notice design issues or weaknesses in the structural integrity of the site and to predict failures such that preventive maintenance can be done to minimize damage. Architects and engineers can also manipulate 3D models in a way they often can’t with 2D CAD drawings. Professionals are able to test what-if scenarios with their designs in 3D, helping to validate their plans and identify any problems with design quality. In addition, these types of models can also give architects and engineers an accurate picture of how they can change their designs if they need to. Because of the accuracy and flexibility of 3D models, architects and engineers are able to spend less time on the design stage of their projects and more time on the actual completion of each task. Professionals are able to identify any issues ahead of time by using 3D modeling, saving them from having to rework schedules and increase budgets. 

With this project completed successfully, the SBL BIM – LiDAR team once more proves that it has the technical acumen and skills to overcome the toughest of challenges in completing projects in their domain of expertise.

GIS 3D feature extraction and modelling for railways using LIDAR technology

With the global drive towards Building Information Modelling (BIM) compliance gathering pace, we have seen an increased requirement for highly accurate and detailed geospatial data within utilities and engineering projects. This has resulted in a corresponding increase in the application of LIDAR technology within these sectors.

In particular, railways have a wide range of complex and critical assets that extend across the rail network. LiDAR survey point cloud data and feature extraction has become an accurate and effective means of modelling, sharing, managing and storing large asset information databases of this sort.

The primary benefit of LIDAR technology lies in its ability to capture assets in 3D. For rail networks, train mounted LIDAR equipment is on the increase because it allows accurate, dense point clouds to be acquired along with the RGB images from the in-cabin video. Having worked on a number of such high-profile projects, SBL has built up a large team of LIDAR modellers and analysts who can process and extract the full range of railway features and assets.

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Fig 1 A wire frame model of a sample gantry derived from LIDAR point clouds

Railway assets include electrical network and equipment, telecommunication network and equipment, tracks and related features, signal systems and station assets. The advantage of deriving 3 dimensional data from LIDAR point clouds is that it will be accurate to a millimetre level. All the peripheral equipment can be modelled in a 3D environment as well. Along the tunnel sections of the network LiDAR is the only technology that can provide comprehensive, highly accurate information on assets.

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Fig 2 station and its component assets as captured from LIDAR point clouds

Mapping to high detail and then rendering in various model platforms is the key to usage of LIDAR based capturing of railway assets. All features within the corridor width of the tracks can be captured to the same very high detail. Stations have innumerable assets components but all these components can be captured effectively using LIDAR point clouds. The creation of a real world situation using 3D models has attracted the attention and approval of many of SBL’s stakeholders and partners. Our LIDAR analysts have achieved these impressive results through 3D wire frame and surface modelling. These models were created and delivered in global GIS formats such as .dgn or .dwg or gml.

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Fig.3 A tunnel section captured from point clouds

This kind of LIDAR technology based railway mapping will benefit different disciplines in different ways. This database will act a pre indicator for drivers and is of great help in their training. For construction engineers it is a monitoring mechanism. For design engineers it the only information provide kinematics of the entire system. For maintenance and operation teams it is a database where they can pinpoint hot spots where repairs are required allowing ground staff to be sent directly to those locations. Using this database a virtual railway stations and tracks can be created for various applications. Overall asset and facilities management will be greatly improved with such a 3-dimensional database.

ROAD MAPPING USING MOBILE LIDAR APPLICATION

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Management of road networks and its associated assets is a huge task for all transportation agencies around the world. The traditional ways of collection of data was time consuming and the productivity of the staff was at the best average. With the advent of new technologies like Mobile LiDAR Mapping, the collection, assimilation, analysis, design, construct, maintain and storage of data has become very fast, accurate and cost sensitive This dynamic system of mensuration can be used to acquire highly accurate and compact 3D statistics by safely driving a gathering vehicle at public road speeds

Mobile Lidar systems is made up of of four different devices: a laser, a scanner, a photodetector and a GPS/IMU positioning system. These four systems assimilate to measure the distance to an object by lighting the target with throbs of light from the laser instrument and gauging the time it takes for each throb to bounce back to the sensor. For the reason that light moves at a persistent and known speed, the distance between the laser instrument and an entity can be measured with a high degree of accuracy. A briskly firing laser can cover entire scenes in a few strokes, visualizing the topography and extracting data. A highly capable processing software then transforms raw data into colorful 3-D point clouds, and the end results are multifaceted, high-resolution maps.

Collection of detailed, high accuracy street-level data of street framework is expedited by having instruments capable of reading GPS/IMU on a automobile platform, achieving immense extents of GIS-friendly LiDAR point cloud data in short amount of time. Terrestrial LiDAR system is competent to see data flanked by buildings and under tree canopy, which airborne systems, will never be able to capture. Mobile mapping systems usually collect a full 360-degree FOV at a speed of 30-40 kilometers per hour. The data collected will be highly precise and can be used to generate convincing 3D data and highly accurate road maps.

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Geo-referencing factor of mobile mapping is akin in idea to airborne platforms, but distinctive in run through. It is not possible to keep the GPS lock all the way through an entire mobile survey. The mobile platform must be able to traverse, and accurately map, below tree canopy, under bridges, without loss of positional accuracy. The IMU system become judgmentally important. The airborne system necessities for GPS/IMU quality basically do not apply in these situations.

As is the case with every mapping application, supplementary imagery and substantial data post-processing is often crucial to producing GIS or CAD-compatible final products. The processing applications necessitate solid surfaces with unambiguously defined edges, and topologically-structured feature objects (points, lines, and polygons) with widespread data description and attribution. The time and effort involved in taking out these features from LiDAR point clouds has been a process only knowledgeable persons can work out. This needs special LiDAR/Point cloud processing software and custom data formats, as is for airborne or fixed systems, transfers over to mobile systems and is augmented by the steep quantity and intricacy of the data acquired.

Incorporation of mobile mapping data, generating exhaustive data for highways and metropolitan infrastructure) with mobile topographic LiDAR survey generated terrain models and oblique imagery is sure to turn into the gold standard for street mapping and visualization in the very near future.

Automatic Acquisition of 3D City Model

CONCEPT PAPER ON AUTOMATIC ACQUISITION OF 3D CITY MODEL
3D city
Introduction

Urban areas are bestowed with a variety of geo information. Some of this spatial information is virtual and some are very complex physical information. Virtual information includes administrative boundaries such as municipal boundary, electoral ward boundary, jurisdiction boundaries of various departments etc. Physical information includes transportation network, communication network, utility network, land use land cover features, hydrographic network etc. Any kind of city management needs a data base containing the spatial information. All queries related to a city or urban management system will definitely relate to some location or spatial reference. Though two dimensional maps are available for the purpose, 3D city viewing is rapidly gaining acceptance and application over the former in most of the smart city initiatives. Application of 3D city models ranging from sitting of tower locations of cell phone companies to micro climate, environmental, air pollution, architectural designs and urban heat wave analysis.

3D city models are digital three dimensional models of urban areas with all its features. These features include aforesaid virtual features as well as other physical furnishings such as buildings, land marks, vegetation, infrastructure landscapes and terrain. These features can be represented in spatial data base in the form of two dimensional as well as three dimensional features class. 3D city models are visually integrating various geo spatial elements to real world scenarios with all its complexities. Creation of automatic 3D city models involves preparation of data base based on the required level of details, storage of the same in a data base, construction and detailing of city models and visualization of the same through various application platforms.

Application of 3D city models

There is a verity of application for 3D city models. Unlike last decade where 2D data base were in focus, current decade witness 3D data base model applications in many developmental activities. Some of the key applications of 3D city models are listed below.

Master planning applications: 3D city models are an apt tool for the town planners. 3D city models will help planners from conceptual development stage to the completion and long term environmental impact assessment stage. The benefit of 3D city models in two planning is that it will reduce planning errors and hence monitory loss, it provides better visualization capabilities and hence monitoring is very easy. The most import part played by 3D city models in such application is that it will greatly reduced project delays. It can also add value as a depicter which can monitor illegal vertical growth of the city.

Spatial Database Infrastructure Applications: 3D city models are part of the spatial data base infrastructure of the region. Irrespective of the initial cost of preparing such data base, in the long term it will be the robust data base service in the back ground of all developmental activities. This will be a base data for further spatial data simulation and modeling. Site suitability analysis of roof top for solar panel installations, thermal emission analysis of buildings and urban fabric are some of such analysis.

Disaster Management applications: 3D city models are highly helpful in disaster management in terms of providing near realistic effected of the disaster. It can simulate fire, flood, bomb explosions etc and after effects on the same in urban infrastructure in place to a great precision. Such simulations will help administrators and planners in managing their rescue operations and resource mobilization.

Navigational applications: 3D building models and terrains will give a realistic display of the area for people navigating and crossing through the area. It is gaining widespread acceptance than simply looking at the locations. It can be act as a tool for intelligent transportation system.

Utility design applications: 3D city models will play a key role in designs of sewerage channels, railway line routing, highway routing, power transmission lines routing and other geo designs.

Archaeological applications: Historical buildings and cultural heritages can be modeled to a highest level of detail (LoD5) in 3D city models so that its intricacies will be preserved and will help in maintenance and repairing.

Decision support system applications: 3D city models can be aptly used by planners and managers to take informative decisions using interactive models.

Property management applications: 3D city models can be effectively used for property management and also for dispute settling tool.

Entertainment applications: 3D city models can be back ground and give virtual reality for gaming industry.

Preparation of 3D city model data

There are several kind of input used for preparation of 3D city models. These are ranging from very high resolution stereo aerial photographs to high precision lidar data to high resolution satellite images. The kind of input used and level of details (LoD) extracted are solely based on the requirement of 3D city model. Buildings are most important items in a 3D city Model. The LoD is mainly for the detailing of the buildings. There are five levels of standard LoDs and these are

  • LOD 0: 2.5D footprints
  • LOD 1: Buildings represented by extruded foot prints
  • LOD 2: Extruded foot prints with standard roof structures
  • LOD 3: Detailed building architectural design models
  • LOD 4: Architectural design models with interior designs

Type of data in 3D city Models

3D city models have much kind of data. These include but not limited to GIS data, CAD data, BIM data, and some of the non spatial data as attributes. GIS data is very much required for any 3D city model. Base maps, digital elevation models, digital terrain models, land use maps, and administrative boundary maps, road network data etc. are the GIS component of the 3D city model. CAD data play a key role in 3D city models. Most of the buildings with required level of details as mentioned above can be designed in a CAD environment. Other infrastructure such as railway network, road network and other utilities can be modeled in CAD environment. The outcome in the form of wire frames or surface models will add flavor to the 3d city models. BIM data will provide with one step ahead information of CAD data. The specifications of the buildings in the form of attributes can also be incorporated in CityGML data model.

Data Management

3D city models comprise a variety of data sets, in a variety of formats. So there cannot be a uniform data base schema for the same. The data base schema will be depending on the requirement and application of the models. The component GIS data sets can be raster rata sets as well as vector data sets. 2D raster data sets can be in .geotiff, .img, .jpg like format based on the rendering capacity of the hardware as well as software and internet capability. 3D models again in ASCII, tiff or obj format where common CAD, GIS and graphic software can render and display. Reliable data base management is a prerequisite for complex analysis and result derivation from 3D city models. So a relational data base is very much required for running and utilizing any 3D city models. In this relational 3D city models, hierarchically structured, multi scale data can be stored, retrieved and analyzed. For Updation of the 3D city model data based is also possible only through a relational data base. Also the relational data base should able to hold multiple formats of the data and its rendering. Open geospatial consortium standards and norms can be followed in 3D city models creations as well. Such data bases are referred as city GML

Visualization of 3D city models

One of the major challenges in automatic acquisition of 3D city models is its visualization and semantic rendering. Visualization is a challenge because component of a 3D city model will be mostly from different data sources such as geo data, GIS data, BIM, CAD etc. So a common frame work schema based on the requirement of the model need to be established beforehand through a relational data base. Visualization and rendering are based on the core functionality required through interactive application suites. Typically 3D city model rendering should be fast, scalable, and cost effective. In case of 3D geometry and 3D texture display a specialized algorithms need to be established. In general rendering can be service based or maps based. City GML will provide not only the visual 3D city models but attributes can also be viewed.

Service based rendering: This kind of rendering will based on data base architectures which runs on client applications which is external in nature. In this case all 3D portrayals of computer graphics and other GIS data sets will be the responsibility of the external application. This can be Open Geospatial Consortium (OGC) compatible. Services based rendering can be of Web 3D service or Web View Service. The former will be based on the client application which is converted the scene graphics and other geo data in 3D models and later is through server based services. In this case the server will generate 3D views which are uploaded on requested to screen and streamed based on the scale of rendering. Here client applications will generate 3D models and save in the server and rendering is not directly done by application.

Map based rendering: Smart 3D city models are following map based approach and render 3D visualization to any kind of platforms. In this case the 3D city model is arranged to a series of small tiles which enable automatic 3D rendering. Different levels of details of the model and pre arranged in a server which any number of concurrent clients can access. Map based rendering is more simplified in terms of data transfer and complexity of the 3D data models.

Building Information Models (BIM)

Buildings are the major and complex component of any of the city model data base. Building information model can be from simplest extruding footprints (LoD1) to very complicated architecture with interior designs (LoD5). Automatic extraction of buildings is possible from 3d laser point clouds and photogrammetric approaches. But through this kind of approaches only LoD1 and LoD2 is possible. For higher levels of details Digital terrain models and digital surface models are required. Operator interference and statistical approaches are required for higher level of details of the BIM.

Role of GIS in disaster management

Natural disasters and catastrophes bring to light major dares for federal controls and local authorities. Earthquakes, floods, cyclones, epidemics, tsunamis, and landslides have become of regular occurrence many parts of the world, continually taking a heavy toll of life and property. Under serious disaster conditions, the major task for establishments is the protection of life (both human and animal), property, and the dynamic life-supporting infrastructure necessary for disaster alleviation. To give an edge in preparing and management of disasters, GIS technology could provide a crucial inputs for preparing a decision support and management system for authorities at times of disaster-related crises.
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Over the past few eons, Space expertise and Geographic Information Systems (GIS) Applications have become obligatory part of the modern information civilization. As the frequency of disasters become more and more regular and penetrating, the demand for these technologies is swelling in order to save lives, to minimize economic losses and to build resilience of the disaster affected region. It is imperious that the policymakers and decision makers make determined efforts to widen and expand the use of space technology and GIS applications in catastrophe prone areas to diminish the effect of disasters.

ROLE OF GIS TECHNOLOGY
GIS technology is a crucial component of information, communication, and space technologies (ICST), enabled disaster controlling systems because it remains predominantly untouched during disasters unlike in the instance of both information and communication technologies which are based on ground arrangement are wide-open to natural disasters.

The scope of GIS in disaster administration is as follows:

  • A large volume of data can be collected.
  • Data collection can be focused across a widespread area.
  • Data accuracy can fit in to the purpose of application.
  • Transfer of data is more consistent and safe even during disasters.
  • Communication is faster in various locations.
  • Communication is reliable across a wide area and remote distances.

TECHNOLOGY OPTIONS
The wide continuum of ICSTs used in disaster alertness, alleviation, and supervision include:

  • Airborne Remote sensing;
  • Geographical Information System (GIS);
  • Global Positioning System (GPS);
  • Satellite navigation system;
  • Internet, e-mail; and
  • Special software packages, on-line management databases, disaster information networks.

Range of Applications
The following phases of Disaster management areas are the point of interest for professionals who hinge on ICSTs for critical solutions.

  • Database generation
  • Information assimilation and analysis
  • Disaster charting and consequence simulation
  • Hazard valuation and observing
  • Disaster tendency forecasting
  • Susceptibility assessment
  • Emergency response decision support
  • Logistics preparation for disaster relief
  • Needs calculation for disaster reclamation and reconstruction
  • Risk analysis and assessment

Data integration is one of the strongest points of GIS. In general the following types of data are required:

  • Data on the disastrous phenomena (e.g. landslides, floods, earthquakes), their location, frequency, magnitude etc.
  • Data on the environment in which the disastrous events might take place: topography, geology, geo-morphology, soils, hydrology, land use, vegetation etc.
  • Data on the elements that might be destroyed if the event takes place: infrastructure, settlements, population, socio-economic data etc.
  • Data on the emergency relief resources, such as hospitals, fire brigades, police stations, warehouses etc.

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When an emergency strike an area, the already amassed spatial data can be effectively used to combat the disaster. Unfolding impact influence area, marking of areas in harm’s way and mass notification can be possible through GIS. Optimizing shelters, routings, estimating effected population and property, assessing quantity of relief materials, advance warnings to nearby possibly affected areas etc will be ease out with the help of GIS
GIS will act as a central database repository during the recovery phase of a disaster. GIS coupled with remote sensing will act as an apt tool in assessment of damage and losses incurred. These kind of spatial data assessment give information on the extent of damage to individual properties and aerial coverage of the damage. This will enable the planners and decision makers to estimate the reconstructions cost, prioritizing the areas for development.