Object contour lines, geometric parameters, attributes and a graphic representation — in the agreed format.
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What the service is used for
Accurate maps
Object contours give shape and location for urban planning, transport routes and natural resource assessment.
Visual representation
Smoothed contours make cartographic images more readable and easier to perceive.
Spatial data analysis
Contours are used to calculate area, orientation and shape of objects and to identify relationships between them.
Navigation and orientation
Contours of roads, rivers, lakes and buildings help choose a route.
Need to extract object contours?
Describe the territory, the task and remote sensing data requirements. A specialist will assess the archive and the scope of work.
What you will receive as a result of the work
01
Object contour lines
A set of contour lines that describe the shape and size of objects on the ground.
Contours are represented as vector data: each line is a sequence of points in an agreed coordinate system.
What you get
- vector contours of object boundaries
- export to the required formats, projection and coordinate system
- georeferencing of contours to ground coordinates
The layer composition, format and coordinate system are fixed in the terms of reference.
02
Geometric parameters and attributes
Area, perimeter, length, width and object attributes: type, name, classification and other characteristics.
Parameters are used to analyse and compare objects on the ground together with vector contours.
What you get
- geometric parameters of objects
- attribute data per the agreed classifier
- a layer for analysis in GIS
The set of attributes is agreed in the terms of reference.
03
Map and set of materials
A graphic representation of objects with contour lines and attributes, an orthophotomap for refinement and a technical report.
Results are transferred on electronic media or via FTP; textual materials are also duplicated in printed form.
What you get
- a map or another agreed graphic format
- digital orthophotomaps for information refinement
- a technical report on the work performed
The need to develop and approve editorial and technical instructions is confirmed before the contract is signed.
How the work goes
Cost and timeline
The cost includes remote sensing materials if needed, creation of a digital orthophotomap for refinement and thematic processing — extraction of contour features.
- consultation — free of charge
- image selection, preliminary analysis of source data, additional and reference materials — free of charge
- ordering images (if needed) — from $0.5 to $70 USD per 1 km²; depends on the survey (archival or new, mono or stereo, resolution)
- the order steps give another RS procurement guide: from $8 to $70 USD per 1 km²
- digital orthophotomap creation — from $1 USD per 1 km²; calculated individually and depends on the volume of remote sensing data processed, presence or absence of ground control points and the DEM used
- extraction of contour features — from 200 rubles per 1 km²; depends on the complexity category and execution timelines
- order guide — from 30,000 ₽
- turnaround — from 20 working days; depends on volume, complexity category, availability of remote sensing materials, additional and reference materials
- in the FAQ the minimum thematic processing timeline is from 10 (ten) working days; delivery of finished products — from 5 (five) working days
- orthophotomap creation for contour feature extraction — from 5 working days from the date of receiving 100% advance for remote sensing materials
- start of thematic processing — from 3 days after orthophotomap creation starts
- payment: 100% prepayment by invoice after signing the contract; start of orthophotomap work — after 100% advance for remote sensing materials, bank transfer only
Order guide — from 30 000 ₽
Contour extraction — from 200 rubles per 1 km²
Turnaround — from 20 working days
What is needed for a quote
To agree the terms of reference and estimate contour feature extraction, please send:
- the area of interest (coordinates, district/region name, shapefile, etc.)
- product requirements (scale, creation or refinement)
- requirements for remote sensing data, additional and reference data
- work deadlines
- the specific task to be solved using the product to be created
If the listed information is not available, describing the intended use of the resulting data is enough.
Specialists will analyse the requirements and propose an optimal option.
Why Innoter
Prompt access to archives
Suitable archival materials can be obtained faster if the required territory and date are already held by operators.
No aviation clearances
A new satellite survey does not require flight clearances typical of aerial surveys.
Large and remote territories
A single satellite pass covers significant areas and makes it possible to work with hard-to-reach districts.
Direct agreements with operators
Distribution agreements help select archival imagery and order new surveys from different suppliers.
Software and server infrastructure
Modern software and capacity for quality control and processing of large data volumes.
Experienced specialised staff
Years of experience on complex projects and specialists in cartography, photogrammetry and remote sensing.
Detection methods, survey and source data
The composition depends on the task, archive or new survey and contour requirements.
1
Object contour feature extraction (object detection) is finding instances of objects in an image. Recognition establishes that an object is present and determines its location.
An object is defined by searching for contours: mathematical methods find points where brightness changes sharply and organise them into curves — edges, boundaries or contours.
2
Contour features provide information on the shape, size and location of objects on the ground for cartography and geospatial analysis.
They are used for accurate maps, terrain analysis, resource planning and management, GIS work, navigation and routing — from urban planning to ecology and tourism.
3
- creating accurate maps: contours give the shape and location of objects for urban planning, transport routes and natural resource assessment;
- improving visual representation: smoothed contours make the map more readable;
- spatial data analysis: area, orientation, shape and relationships between objects;
- navigation and orientation: boundaries of roads, rivers, lakes, buildings and route selection.
4
The main material is archival remote sensing data from satellite operators for the most current date or a new survey. Additionally, geographical descriptions, maps and atlases of larger or smaller scale, directories and the client's data are used.
If the information is insufficient, specialists analyse the intended use of the data and propose a solution.
5
The overall timeline in the table is from 20 working days. Stage composition:
- issue alignment and analysis of remote sensing data availability — from 1 to 5 days;
- contract signing — from 1 to 5 days;
- image acquisition — from 3 to 10 days from the date of 100% advance for remote sensing materials;
- receipt of source material (paper tablet or scanned) — from 1 to 20 days;
- digital orthophotomap creation for refinement — from 5 days;
- thematic processing — from 15 days;
- quality control — from 5 to 10 days;
- technical report — from 5 to 10 days.
The timeline depends on the area in km², product type and availability of the remote sensing archive.
6
Before the contract: express assessment of the purpose, technical issues, remote sensing archive and service feasibility; then the ToR, methodology, coordinate system and projection, labour, timelines and cost.
After 100% advance for remote sensing materials: ordering and incoming control of images, check of the client's materials, approval of editorial and technical instructions, creation of digital orthophotomaps, thematic processing (contour feature extraction), visual and automated quality control, export to the required formats and a technical report.
Frequently asked questions
Answers to key questions about service parameters, timelines, deliverable formats and workflow. If you did not find what you need — contact us and we will help.
- area of interest (location / coordinates of the object in any convenient form, and the area of the object);
- the specific task to be solved with the application of the product to be created.
As the main material for creation (update) of the data project the RS data available in archives of spacecraft operators for the most current date are used, or new imagery is ordered. In addition, when creating (updating) the data project, additional and reference materials are used in the form of various geographical descriptions, large (small) scale maps and atlases, reference books, as well as data available to the Customer.
- Terms of thematic processing based on space or aerial survey data depend on the volume and complexity of the order. Minimum term - from 10 (ten) working days;
- Terms of delivery of finished products from 5 (five) working days.
100% prepayment by invoice after signing the contract.
Object detection in an image is the process of automatically identifying and localizing various objects or regions of interest (ROI) in a digital image. This is a crucial task in the fields of computer vision and machine learning, finding applications in various areas, including automatic face recognition, vehicle detection, medical diagnostics, image annotation, robotics, video surveillance, and more.
Various methods and algorithms are employed for object detection in images. Some of the most popular methods include:
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Classical computer vision methods:
- Haar Cascade Methods.
- Edge and contour processing methods.
- Template matching methods.
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Machine learning methods:
- Feature-based object detectors (e.g., HOG, SIFT, SURF).
- Machine learning-based object detectors, such as Support Vector Machines (SVM), Random Forests, Neural Networks, etc.
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Deep learning:
- Convolutional Neural Networks (CNN) are the most popular method for object detection in images. Networks like Faster R-CNN, YOLO (You Only Look Once), and SSD (Single Shot MultiBox Detector) provide high accuracy and detection speed.
The object detection process in an image typically involves the following steps:
- Load the image.
- Apply the object detector (e.g., a neural network) to search and localize objects.
- Determine the classes of objects (e.g., "cat," "dog," "car").
- Visualize the results with annotated bounding boxes or object labels.
Object detection in images is a crucial component of many applications that require the analysis of visual information. The accuracy and speed of detection may vary depending on the chosen method and model, so selecting an appropriate approach depends on specific requirements and the context of the task.
Detecting objects in images used in cartography is crucial for creating accurate maps and Geographic Information Systems (GIS). In cartography, object detection in images may involve the following tasks:
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Road and Infrastructure Detection: Roads, bridges, bus stops, and other infrastructure elements can be automatically identified in images using computer vision and image processing methods. This can aid in creating and updating road maps.
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Building Detection: To create maps of cities and settlements, it is important to determine the location and outlines of buildings in images. This can be useful for urban development planning and property assessment.
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Natural Object Detection: Identifying mountains, lakes, rivers, and other natural features in images helps create more informative maps of the environment and rural areas.
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Boundary and Geographic Object Detection: For creating administrative maps and maps of country and region borders, detection methods can be used to automatically locate and mark boundaries and geographic objects.
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Transport and Motion Detection: For monitoring traffic movement on roads and railways, as well as determining traffic density and flow, object detectors based on video stream analysis can be employed.
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Point of Interest Detection: This may include detecting objects such as geodetic beacons, traffic lights, road signs, and other elements crucial for navigation and orientation on maps.
To accomplish these tasks, both classical computer vision methods (e.g., edge detectors, image segmentation) and modern deep learning methods, such as Convolutional Neural Networks (CNN) and object detectors trained on large datasets, can be used.
When detecting objects in images in cartography, it is also important to consider the georeferencing of data to correctly position objects on the map according to geographic coordinates.
Need satellite imagery or help choosing the right data?
Describe your task, area of interest, and desired outcome. We will check data
availability, assess new acquisition options, and propose the optimal solution in terms of timelines, cost,
and quality.
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