Creating 3D Visualizations in Support of the Planned Expansion of the National Museum (Kraków, Poland)

Creating 3D Visualizations in Support of the Planned Expansion of the National Museum (Kraków, Poland)

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Case studies

Creating 3D Visualizations in Support of the Planned Expansion of the National Museum (Kraków, Poland)

Client

Industry

Survey & Inspection
Energy & Utilities

Region

Europe

Technology

Ground Penetrating Radar (GPR) – Lecia DS2000
Detector – Leica Ultra Advanced
GNSS solution – Leica CS20/GS16
Terrain Laser Scanner
CAD, GPR and 3D modeling software

Overview:

The former Cracovia Hotel, that was originally built in the 1960’s sits across the street from the National Museum in Kraków (MNK) and is slated to become the new Museum of Architecture and Design. Poland’s culture minister has unveiled plans that outline the transformation of the old hotel (now abandoned) into a modern museum under the direction of the NMK.

GISonLine was pleased to be involved in supporting MNK with the planning and analysis for the transformation of the Cracovia hotel by conducting underground utilities detection and above ground terrain scanning to produce 3D models and digital representations of the project areas.

The Impact:

GISonLine collected and processed data from multiple scanning platforms and sensors to obtain accurate measurements and data in support of the planning and analysis of the museum expansions and the safety of construction crews. Data collections included:

  • Terrain Laser Scanner (3D models)
  • High-resolution photos using Drones
  • Ground Penetrating Radar (GPR)
  • Advanced sub-surface detectors

The Outcome:

GISonLine successfully completed the project surveys and scanning to produce highly accurate 2D maps and 3D models showing the underground utilities that exist between the existing National Museum complex and the old Cracovia hotel. GISonLine processed the terrain laser scanning data using CAD and 3D modeling software to generate a digital representation & 3D model of the old cracovian hotel for analysis and site visualization.

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Identifying & Mapping “Safe Locations” for Borehole Drilling on Nile Street (London, UK)

Identifying & Mapping “Safe Locations” for Borehole Drilling on Nile Street (London, UK)

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Case studies

Identifying & Mapping “Safe Locations” for Borehole Drilling on Nile Street (London, UK)

Client

Industry

Survey & Inspection

Region

Europe

Technology

Ground Penetrating Radar Lecia DS2000
Detector – Leica Ultra Advanced
GNSS solution – Leica CS20/GS16
CAD and GPR software

Overview

Nile Street in Hackney is a high-density development area located approximately 2 miles north from the center of London. As an urbanized area, any excavation or ground digging would be highly dangerous without prior identification of underground utilities.

In support of the new development and investment planning on Nile Street, it was necessary to conduct borehole drillings to determine the ground structure and soil compositions. GISonLine was pleased to support the Greater London Authority (GLA) with underground detection and survey services that supported the safety of the drilling crews and identification of safe locations for the drilling of the boreholes.

The Impact

GISonLine performed the underground detection surveys using ground penetrating radar (GPR) and advanced detectors to establish surface markings that delineated “safe locations” where the drilling of the boreholes could be conducted.

 The Outcome

GISonLine successfully completed the underground detection survey and provided the data in 2D and 3D formats for a clear understanding on the exact location of underground utilities. Using the 2D maps and 3D models depicting the underground utilities and sub-surface objects, the planning of where to  conduct the borehole drilling and inspections was selected and identified prior to any drilling operations.

The project represented the high requirements to provide accurate location identification of sub-surface utilities for the prevention of damages to underground infrastructure and ensuring worker safety.

 

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Determining Subsurface Rock Depths & Locations For Utilities Routing (Stockholm, Sweden)

Determining Subsurface Rock Depths & Locations For Utilities Routing (Stockholm, Sweden)

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Case studies

Determining Subsurface Rock Depths & Locations For Utilities Routing (Stockholm, Sweden)

Client

Industry

Survey & Inspection
Energy & Utilities

Region

Europe

Technology

GPR – Lecia DS2000
Detector – Leica Ultra Advanced
GNSS solution – Leica CS20/GS16
CAD and GPR software

Client: Exact
Location: Stockholm, Sweden

GISonLine was pleased to take part in an underground survey detection within the suburbs of Stockholm, Sweden near a housing estate in the town of Hasthagsterrassen.  The project utilized Ground Penetrating Radar (GPR) equipment to collect survey data  and generate 3D models for the placement of new utility lines in support of housing estate infrastructure.

The Impact

The focus of this project was on determining the sub-surface ground composition and depth of rocks to indicate the optimal placement of new utility lines and cables.  GPR surveys were conducted using the Leica DS 2000 ground penetrating radar, which is a noninvasive means to measure rock depths up to approximately 6 meters depending on ground conditions.

The Outcome

GISonLine successfully completed the underground surveys and delivered detailed 2D mapping and 3D model products that made it possible to precisely define the locations of rock structures and their depth. As a result of the survey efforts, field crews were able to identify and designate the positions where new utility lines could be placed with the lowest possible excavation efforts and avoidance of rock structures.

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3D Modeling & Mapping of Underground Utilities in Support of Fiber Optic Routing (Hamburg, Germany)

3D Modeling & Mapping of Underground Utilities in Support of Fiber Optic Routing (Hamburg, Germany)

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Case studies

3D Modeling & Mapping of Underground Utilities in Support of Fiber Optic Routing (Hamburg, Germany)

Client

Industry

Survey & Inspection
Energy & Utilities

Region

Europe

Technology

GPR – Lecia DS2000
Detector – Leica Ultra Advanced
GNSS solution – Leica CS20/GS16
CAD and GPR software

Client:  FONBUD
Location: Hamburg, Germany

GISonLine was pleased to take part in a long-term project for the city of Hamburg, Germany. The project covered an area of around of 200 square kilometers with a focus on performing underground detection to check and validate that the planned route of newly designed fiber optic cables would not collide with the existing utility networks.

The Impact

GISonline performed the underground detection surveys using ground penetrating radar (GPR) to identify and locate all existing lines of utility services. The survey work was conducted in various environments to include near roadways, railways, and urbanized areas.

The Outcome

The results of the underground survey included the development and delivery of 2D utility maps and 3D models that outlined the location of existing utility networks. The mapping products were supplemented with information obtained from the newly designed plans for fiber optic networks, which allowed for combining with the underground survey datasets to provide additional situational awareness and analysis of the designed fiber optic routes.

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AI and ML-based service for traction network inventory and monitoring

AI and ML-based service for traction network inventory and monitoring

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Case studies

AI and ML-based service for traction network inventory and monitoring

Industry

Energy & Utilities
Survey & Inspection
Consumer Applications

Region

Worldwide

Technology

Artificial Intelligence

Purpose / Application

 

The AI (Artificial Intelligence) and ML (Machine Learning) -based traction network inventory and monitoring service is a comprehensive solution offered by GISonLine. The service includes conducting aerial surveys and processing data collected using unmanned aerial vehicles (UAV). It takes advantage of all the benefits of professional UAV systems without unnecessary risks and costs.

Artificial intelligence and machine learning methods enable the automation of processes for identifying and locating traction network components. This results in:

  • Reduced time for inspection/analysis
  • The ability to perform inspections/analyses remotely
  • Increased safety by reducing the number of “manual” inspections in trackside areas
  • Fewer failures thanks to early detection of damages

The method can also be used, for example, to estimate damage caused by transportation accidents in real time or to assess the quality of completed service works and renovations.

Innovative approach

During the pilot implementation an innovative approach to data preprocessing was developed in collaboration with researchers from Wrocław University of Science and Technology. In particular, heatmaps were used to identify spatial sequences with higher likelihood of encountering the desired traction elements. Group analysis based on object hierarchy was performed to detect small but important components of the traction network. This involved searching for small network elements (e.g. hangers or insulators) in a specific spatial relationship to larger, easier-to-identify objects (e.g. support structures). At GISonLine, we added to that an algorithm for automatically assigning geographic coordinates to the identified objects.

Performance of the method

The effectiveness of the AI-based traction network inventory and monitoring service depends on the quality of the data used during model training as well as the data collected from the aerial survey.

During test implementation, a high accuracy in object recognition was achieved. The total inference time was approximately 900 ms per image.

Further directions of the implementation and development of the method

The method applied can be enhanced and developed through the use of laser scanning data (Lidar) or by increasing the pool of objects used for training. Ultimately, full automation of the process is achievable.

In terms of thematic development, the method could also expand to address issues such as:

  • Location of wooden sleepers awaiting replacement with concrete ones
  • Assessment of railway ballast quality to determine the need for replacement
  • Assessment of the degree of wear of electric traction cables
  • Information on the precise routing of electric cables

Comprehensive service

GISonLine possesses its equipment, software, and copyrights for the developed method. Through the pilot project, our team has acquired the necessary skills in applying machine learning tools. Combined with our previous experience in network industry implementations, this prepares us to handle the entire process of service delivery.

Need a fresh but reliable solution for supporting management of your network assets? 

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