{"id":23199,"date":"2024-01-12T13:27:32","date_gmt":"2024-01-12T12:27:32","guid":{"rendered":"https:\/\/nr.no\/en\/?post_type=bc_area&p=23199"},"modified":"2025-09-30T12:50:23","modified_gmt":"2025-09-30T10:50:23","slug":"mapping-and-map-revision","status":"publish","type":"bc_area","link":"https:\/\/nr.no\/en\/areas\/mapping-and-map-revision\/","title":{"rendered":"Mapping and map revision"},"content":{"rendered":"\n

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We specialise in the research and development of algorithms specifically designed for mapping and map revision. Drawing on data collected from satellites, aircraft and drones, we are able to develop tailored methods that are applicable across various domains, including infrastructure, climate and even archaeology. <\/strong><\/p>\n\n\n\n

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We use deep learning to map vegetation height across the African continent based on Sentinel-2 data. Illustration: NR.<\/em><\/figcaption><\/figure>\n\n\n\n

A map is a visual representation that highlights relationships among spatial elements like objects, regions, terrains and themes. Maps simplify information about the world and play a central role in conveying data at different levels, from overarching information at national level, all the way down to structures, roads and other features at local level. <\/p>\n\n\n\n

Maps are crucial to a country’s security, socioeconomic development and environmental sustainability. National maps include topographic maps, cadastral maps and various thematic maps like landcover, population density and climate. Urban landscapes are constantly evolving, and frequent map revision is needed for effective urban management. Updated maps are used to inform decisions on urban planning, building infrastructure, managing emergencies and overseeing real estate. They are also used to monitor green spaces and address environmental issues in densely populated areas. <\/p>\n\n\n\n

In Norway, mapping and map revision is managed collaboratively between the Norwegian Mapping Authority (Kartverket) and local municipalities, with active participation from various public and private entities, including NR. <\/p>\n\n\n\t

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Extensive experience in mapping methodologies<\/h2>\n\n\n\n

We have been developing mapping methodologies for more than 40 years, and began our work with thematic mapping using Landsat and SPOT satellites in the 1980s.<\/p>\n\n\n\n

In the 1990s, we started using digitalised aerial images to achieve high-resolution forest mapping, and we used data from the European Space Agency (ESA) to test airborne hyperspectral sensors, also known as imaging spectrometers, and synthetic aperture radars (SAR). <\/p>\n\n\n\n

Today, digital aerial imagery is standard, while imaging spectrometers are used on a case-by-case basis.<\/p>\n\n\n\n

For large-scale mapping, different satellite sensors are available, from very-high resolution sensors on commercial satellites, to high-resolution sensors like multispectral imaging (MSI) aboard Sentinel-2, and sensors with moderate resolution based on Sentinel-3 and similar satellites, which are used for daily, global mapping. <\/p>\n\n\n\n

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An updated map based on Sentinel-2 image data shows a suggestion for map updates in an area of wilderness in Norway where wind turbines are being developed. Illustration: NR. <\/em><\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n\n\t
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Automated methods for mapping and map revision<\/h2>\n\n\n\n

In several of our projects, NR is developing automated methods for mapping and map revision, including mapping vegetation height across the entire African continent, identifying new forest roads, detecting cultural heritage sites, classifying tree species, tracking building changes, and estimating birch pollen release. <\/p>\n\n\n\n

Automated mapping methods rely on artificial intelligence. This can be achieved by training deep neural networks with large amounts of labelled training data, or by utilising conventional methods if a well-established approach already exists. This can for example apply to assessments in land cover and land surface temperature.<\/p>\n\n\n\n

The type of remote sensing data selected is determined by four factors:<\/h3>\n\n\n\n