Whether you need images, GPS tracks,
LiDAR point clouds, or POIs, Intetics
can gather any type of data based on
your specifications. Additionally, we
have a seasoned team of mapmakers
and GIS analysts who can handle any
kind of geospatial content, guarantee
top-notch quality, and integrate it into
your database.
Mapmaking
as a Service
All Your Mapmaking
Needs at One Stop
All Your Mapmaking Needs at One Stop
- Images / Video
- LiDAR
- GPS
- POIs
- Road Geometry
- LandUse
- POIs
- Addressing
- Quality Control
- Data
Transformation - Integration
Why Intetics?
- 15+ years in mapmaking
- 100+ GIS engineers and analysts
- 95% of meeting customers’ SLAs
- Collected hundreds
of thousands km across
Europe and North America - Created millions km
of map objects
(road network, landuse, etc.)
- ~ 50 field data collectors across
Europe and North America - ~ 100 clients whom
we helped with geodata
collection and mapmaking - Saved about 30% of cost
for our clients in average.
Thanks to automation
and processes optimization
Team
General Manager
Expert
Group
Managers
SME
GIS
Specialists
LiDAR
Experts
Senior Geodata
Engineers
Quality
Team
Senior Automation
Engineers
Crew 1
Crew 2
Crew 3
….
Data Collection Team
Geodata Engineer 1
Geodata Engineer 2
Geodata Engineer 3
….
Data Processing Team
Automation,
Improvements
R&D
Planning
Route Planning:
- Identifying and analyzing survey
area. All factors considered: road
conditions and accessibility, level
of traffic, and weather - Usage of GIS and Navigational
tools for most optimal route
planning - Coordinating with relevant
authorities and obtaining
permissions - Defining project costs and
timelines
- Identifying and analyzing survey
Data Processing Planning:
- Reviewing data output
requirements - Choosing the most appropriate
software and algorithms - Team allocation
- Setting project KPIs: estimate,
schedule, cost
- Reviewing data output
Equipment Preparation
Selecting the most appropriate hardware setup for the project. Development of cost-effective and high-quality custom solutions.
Light
Survey
Solutions
Mirrorless camera
Laptop / tablet
Action camera
GNSS/INS
Pro
Survey
Solutions
360° camera
LiDAR
GNSS / INS
Crew Allocation
- Core team of experts available
immediately to initiate collection - Attracting, training and
adaptation of field crews - Regular equipment, survey and
safety trainings for the teams to
ensure the highest quality of the
data collected
Data Processing Methodology Determination
Data
Processing
Requirements
Defining requirements for data to be delivered to clients, including:
- Output format
- Quality and Accuracy parameters
- Data protection (e.g., GPDR) requirements
Software
Developing custom software or using existing tools for post processing
Integration
Client’s system specifications and requirements
for following data integration and ingestion
Geography We Cover
* Other countries / regions available on request
Georeferenced Data You Get
Photo / Video
by single or multi-cam
LiDAR Point Cloud
POI Data
Specific Data
for ML/AI
Our Work in Europe Last Year
46,000+ of areas of
interest collected425,431 km driven
28 countries
covered29 crews engaged
751 days in travel
10,400 hours
on the road32 cars rented
24 tons
of fuel spent
What Equipment Set to Use?
 SIMPLE |  LIGHT |  MIDDLE |  ADVANCED |  EXPERT | |
| POI, Geofencing | Lane Markings, Road Infrastructure, Signs, POIs (Rest Areas, Gas Stations, etc.) | Enhanced
Middle set + Road Inventory + Indoor | Enhanced Advanced set + LiDAR | ||
| POI |  | | | | |
| GPS + Photos | Smartphone | 1 Camera | 2 Cameras | Custom | Custom |
| 360°, 1.5-5m GPS Accuracy |  | | | | |
| LiDAR, 360°, Less than 1m GPS Accuracy | | | | | |
Equipment Specs
 SIMPLE |  LIGHT |  MIDDLE |  ADVANCED |  EXPERT | |
| Set includes | Smartphones | Sony x3000,
laptop, GNSS | Sony a6400 (2 cam), laptop, GNSS | Mosaic51 /
Ladybug 5+, laptop, GNSS | Ladybug 6,
Velodyne HDL-32E, laptop, GNSS+INS |
| Camera | 5-12 MP | 8MP | 2x24MP | 6x5MP and higher | 6x12MP and higher |
| FPS | 1 | 1 | 1 | 10 and higher | 15-30 |
| Shutter | rolling | rolling | rolling | rolling / global | global |
| GPS | Build-in 5-50m | U-Blox M8U 2-30m | U-Blox M8U 2-30m | U-Blox M8U / EMLID 2-30 or <1 m | EMLID REACH RS2 <1m |
Economics
Optimal Offer
Processing request
(up to one week)
Types of data to collect
Deadline
Scale of collection
- Our production unit is located in Eastern Europe,
thus Client will get prices lower by 30-40%
comparing to competitors for the field survey.
This is like a well-known outsourcing, but for data collection. - We are ready to start surveying within two weeks
(means Simple – Middle sets) - Also, if you want to test us in work before starting to cooperate,
we can perform a Pilot project for you.
Case Study: Data Collection for Highway Navigation
Client: Perform the large-scale photo surveys of
highways across Europe for Automotive Navigation
project
Challenges:
- To create a database of high-quality pictures of the
roads for vastly-populated areas of 27 countries in
Europe - To develop the start-to-end data acquisition process,
including teams’ formation, equipment selection, plan
survey campaign, data quality assessment, etc.
Hardware:
- Sony a6400 Mirrorless Digital Cameras
- U-Blox M8U GNSS
- Lenovo Thinkpad – Laptop
Results:
- 190 000+ km were surveyed within 3
months, and over 8,5 million high-
quality pictures were collected - 99.97% of the view data received from
Intetics’s field teams met the quality
requirements - The client has been able to save up to
20% of the allocated budget due to the
increased efficiency of the development
processes - Client’s highway coverage expanded by
17%
Map Creation
- Street network
- Geometry
- Routing
- Road priority
- Signs
- Restrictions
- Addressing
- Polygon creation
- Footprints
- LandUse
- Enhancement
- Lanes mapping
- Geocoding
- Maneuvers
- POIs
Metrics
We track the following metrics and parameters to ensure
the highest quality and continuous improvement
- Processing performance
- Completeness & correctness
- Error rate
- Resource efficiency
- Lead time & SLA
- Risks
Output
We provide processed data in following formats:
- Vector data
Road Network, Hydrological Features,
Land and Admin Boundaries - Extracted objects and features
Building Footprints, Municipal, PLS
and Road Infrastructure objects, Parking Lots - Classified objects
Vegetation, Water, Buildings, Wires and Towers - POIs
Restaurants, Hospitals, Gas Stations,
Malls and Tenants
Map editing
Landcover creation
LiDAR classification
2D Vectorization
3D Vectorization
Intetics operates a data center for over 10 years, expanding
the global footprint of the mapping industry leader
Client: The Client is a world leader
and a pioneer in location technology,
having the largest market share
in navigation services, operating across
56 countries.
Challenges: To build a data
processing center to enhance
the Client’s mapmaking capabilities
in Eastern Europe. The Client required
a partner to process geodata quickly
and cost-effectively
Technology Stack:
Proprietary software, ArcGIS, QGIS
Results and Benefits
- The Client expanded in Eastern Europe
and was recognized as #1 company in
the location service industry. - The reliable partner provides
outstanding services, cuts costs, and
improves every aspect of work. - The ODT was staffed with highly
qualified GIS specialists in a mere four
months, which is twice as fast to set up
a similarly functional center.
Car Parking Coverage and Data Accuracy Improvement
for Driver App
Client: American leader company
in Traffic, Navigation and Data
Analytics
Challenges:
- Improvement quality of data in the
data base to get highest accuracy
among competitors - Drastic increase of amount of
parking spots in the client’s app in
short terms - Automation of production
processes where manpower was
required
Results and Benefits
- 180,000 parking locations are added and verified matching highest quality criteria for a bit more than a year
- Speed of verification is increased by 20% due to system analysis and automation of work processes
- Driver app is getting global and high-rated since new regions are updated faster and data get enhanced
Quality Control
Daily and final Quality Checks include:
- Data Validation
Validation collected data for accuracy, consistency,
and anomalies - Image Quality Assessment
Evaluation image clarity, resolution, and exposure - Geospatial Accuracy Check
Vegetation, Water, Buildings, Wires and Towers - Metadata Review
Thorough review metadata for completeness and accuracy - Consistency and Completeness Check
Ensuring data consistency and completeness
Data Transformation into the Required Format
- Coordinate System Conversion
Converting collected GPS coordinates to the desired coordinate system
for consistency and compatibility with other spatial datasets - Data Format Conversion
Converting raw data files from field equipment into a compatible format
for further analysis or integration - Data Cleaning and Filtering
Removing noise, errors, and outliers from the collected data to ensure data
integrity - Attribute Extraction
Extracting specific attributes or information from the data to meet project
requirements.
Automation of our Data Collection
1
Route Planning
Algorithms generate optimal
routes, reducing data
collection time by 25%,
resulting in a 30% increase in
daily coverage and fuel savings.
2
GPS Integration
Automated geotagging with
GPS data ensures precise
image location, reducing
manual geotagging time by
95%.
3
Data Transfer and
Backup
Automation transfers data to a
central storage system,
minimizing the risk of data loss
and ensuring 100% data
completeness.
4
Quality Control
Automated checks enhance
data quality by evaluating
image clarity, GPS accuracy,
and route alignment,
improving overall accuracy by
20% and reducing manual
review time by 70%.
5
Financial Tracking
Integrated automated financial
tracking enhances expense
management, resulting in 95%
accuracy in reimbursement
processes and financial
reporting.
Map Content Creation Automation (Selected Examples)
- SmooothieMaker – the tool aimed to ease work with ADAS geometry. It allows to move nodes,
long paths of road and has improved smoothening logic to meet ADAS requirements. It also finds
manual errors >> Watch video presentation >> - Traffic Signs Toolbar – the tool that increased speed by 15% on work with traffic signs.
Quick panel with buttons for the 62 most frequent feature types. - ML-driven polygon identification – the tool that efficiently processes input data in the form of
PDFs or pictures containing the polygon representation of an entire shopping mall or multi-
polygon on a map - And many more!
Automatic Parking Signs Recognition
Goals:
- Develop a solution for parking signs detection
and recognition of on-sign information. - Data analysis from several on-street images
providers. - Development of an image download and pre-
processing workflow. - Build a solution for parking signs detection
and classification. - Extract text information about on-street
parking from cropped images.The Client is a
world leader and a pioneer in location
technology, having the largest market share
in navigation services, operating across 56
countries.
Technology Stack:
- C++, OCR.Net Component Toolkit, TensorFlow, Keras Library
Results and Benefits
- The algorithms
successfully identified
85%+ of signs on the
streets in the city,
90%+ of the signs
were identified correctly. - The algorithms were
implemented successfully
in at Inrix’s single web
application to automatize
parking signs detection
and reduce
workload significantly.
Detection of Building Footprints
Client:
A US based real estate developer.
Challenges:
- Build solution for feature extraction from the aerial photos
provided by the Client; - Edge angles of the building footprint should be close to 90
degrees, consecutive angles of the building footprint shouldn’t
be very sharp
Technology Stack:
Python, PyTorch
Results and Benefits
- Intetics has developed a working solution
allowing the Client to benefit from the sale of
not only aerial photos but also extracted data. - Building footprints detection was developed
using of semantic segmentation approach and
pixel-mask polygonization. - Building polygons was received by
approximation of the predicted pixels with the
use of the Douglas-Peucker algorithm. - The work resulted in the application allowing
the Client to get building polygons from high-
resolution aerial imagery.
ML Algorithms for the Feature Extraction
of the Urban Road Infrastructure
Client: A German company operating
in urban road infrastructure in 10
European countries.
Challenges: Develop machine
learning algorithms for the automated
processing of the data from the car-
mounted LiDAR+optical mobile
surveying system, including:
- Quality assessment of the horizontal
pavement marking - Identification of the damaged road
signs - Identification of curbstone defects
Results and Benefits
- The achieved accuracy
level was 88-97%
depending on the type
of feature. - The algorithms were
applied in the road
network of 15 German
cities.
Data Anonymization in compliance with GDPR
Client:
A market leader in navigational,
mapmaking and data services.
Challenges:
- The Client requested data collection
services to ensure its maps and
services are in compliance with ISA
regulations in the EU; - The provided data must be fully
anonymized (personal data
removed form images) to be in
compliance with GDPR
Technology Stack:
Python, OpenCV, PyTorch, CVAT
Results and Benefits
- Intetics has surveyed
230,000 KM in CE/EE countries
within 6 months and delivered
data in client’s requested format - Intetics developed an ML model
which automatically “erases”
license plates and human faces
from images - The Client updated its maps so
that it was ready for ISA rules
implementation
Featured Cases
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