Besides providing various services we are continually developing new relevant and qualitative data products. We often use publicly accessible data like satellite images and aerial photos. In some cases, commercial data is also used. Are you interested in one of our data products, or would you like more information about which other data products we can develop? Feel free to contact us.

National monitoring of solar panels (2016-2020)

More and more solar panels are being installed on a national scale to generate sustainable energy. The energy transition is in full swing and that poses several challenges. How much energy can we generate in total with solar panels? What is the prognosis? Where is the capacity of the electricity grid under pressure? By mapping solar panels on a national scale, these questions can be better answered. View this viewer to gain insight into the number of solar panels per inhabitant in each municipality.

We automatically detect the locations of solar panels by using deep learning (artificial intelligence) and public aerial photos (0.25m resolution NL). Because we segment the solar panels accurately, the surface area of solar panels can be determined at the address level. After this an accurate estimation can be made of the total power yield in kilowatt-hour. 

About this dataset:

  • Overview of solar panels (vector) in your entire municipality, province or throughout the Netherlands.
  • Direct insight into panel areas per address, neighborhood or district by linking to BGT, BAG or CBS data. 
  • Based on open data (0.25m resolution).
  • Possibility of monitoring through (historical) data.
  • Possibility to enrich the dataset with annual energy yield at address, neighborhood or district level.

Detection of solar panels (high resolution)

The energy transition is a complex task. Where is a heat network effective and where do we focus on generating sustainable energy? As spatial data analysts, we work a lot on these issues and we script relevant energy transition tools that contribute to this task.

 The amount of existing solar panels, and in particular the surface area is very important information. In order to help municipalities with its energy transition strategy, Urgis, in collaboration with AeroVision, develops a deep learning algorithm (AI) that segments solar panels with very high accuracy. The result is converted into vectors and combined with elevation data to calculate effective panel areas. This gives you as a municipality immediate insight into the number of solar panels at the address, neighborhood or neighborhood level! Are you interested in this product, or do you want to participate in collective purchasing, please contact us.

About this dataset:

  • Overview of solar panels (vector) in your entire municipality.
  • Direct insight into net panel areas per address, neighborhood or district by linking to BGT, BAG or CBS data. 
  • Reliable result of at least 96% detected surface! 
  • Based on very high resolution ortho-mosaic *.
  • Possibility of monitoring through (historical) data.
  • Possibility to enrich the dataset with annual energy yield at address, neighborhood or district level.
    * 4 to 10 centimeter resolution. Data owned by the municipality.
Aerial view with solar panels 
Solar panel output model

Aerial photographs owned by the municipality of Stichtse Vecht

Solar energy potential

Which roofs are suitable for solar panels? How much energy can these solar panels potentially produce? With the help of a 3D model it is possible to accurately determine how much solar energy is generated on a surface all year round. This analysis will make shadows visible caused by trees and dormers. With this information you gain insights in the potential solar energy yield of roofs.

In combination with the solar panel detection (segmentation), we can accurately calculate how much energy the current solar panels can generate and what the share of current solar panels is compared to the potential suitable roofs. In The Netherlands we use the openly availebly digital elevaltion model AHN3 (AHN4 on the way), but also other more recent commercial elevation model can be used.


About this dataset:

  • Based on 3D elevation model (open data or own elevation model)
  • 3D simulation of solar radiation based on sun positions all year round and height model
  • Irradiation at address level by linking to BAG (average irradiance, suitable roof surface, etc.)
  • Available as vector or raster

National pavement / green monitoring (2016-2020)

The Netherlands is a densely populated country in which every square centimeter is used. Due to climate change, the country is increasingly facing the consequences of this. Summers become drier and warmer, but also rain showers become more intense, which can cause flooding. The amount of paved surface and vegetation play a crucial role in this. Vegetation provides cooling in the hot summers and allows rainwater to infiltrate. This can limit heat stress and flash floods.

Effective measures can be taken by gaining insight into the amount and distribution of pavement and green areas. Urgis accurately maps the paved and green surface, both in public space and private property. Historical data can be used to monitor changes and make forecasts.

About this dataset:

  • Overview about pavement / green (vector) and the spatial distribution
  • Direct insight into surfaces per address, neighborhood or district by linking to BGT, BAG or CBS data
  • Based on open data (0.25m resolution)
  • Possibility of monitoring through (historical) data

Overview of addresses with thatched roofs in the Barneveld region

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National dataset thatched roofs

Urgis has developed a machine learning algorithm that detects thatched roofs in high-resolution aerial photographs. Using this algorithm, we have classified, with very high accuracy, the roof type of each individual property in the Netherlands. The thatched roof dataset can be purchased nationally and regionally. A small subset of this dataset can be seen in the interactive map on the right.

This dataset has been specifically developed for safety regions, fire brigade and insurers and is relevant for providing insights in fire hazard risks. We can deliver the dataset in different formats.. If you are interested in this data product or would you like more information, please contact us.

Why the thatched roof data product:

  • All locations and addresses of thatched roofs in your database.
  • Direct overview of risky objects fire hazard.
  • Relevant information for risk management and emergency response.
  • Supplement data product with other information, such as the purpose of use of the building and surrounding buildings, the year of construction and surface.

Direct overview of fire risk objects

Especially for safety regions, insurers and thatchers.

Green roofs

Klimaatverandering, verstedelijking, luchtkwaliteit en biodiversiteit zijn actuele onderwerpen. Voornamelijk in stedelijk gebied kan dit knelpunten veroorzaken omdat de ruimte hier schaars is. Groene daken bieden hierbij veel kansen voor klimaatadaptie en natuur. Veel vlakke daken bieden een groot potentieel aan ruimte voor groen op veelal onbenutte ruimte.

Voor vraagstukken rondom deze onderwerpen is het nuttig om te weten waar groene daken reeds aanwezig zijn en welke daken potentieel geschikt zijn voor de aanleg van een groen dak.

Urgis analyseert alle daken in uw gemeente op basis van openbare luchtfoto’s (0.25m) of op gemeentelijke luchtfoto’s. Omdat bij (hoge) gebouwen vaak een omvallingseffect zichtbaar is op luchtfoto’s, corrigeren wij deze voor omvalling. Hierdoor komt de BAG geometrie gelijk te liggen met de bovenkant van het dak i.p.v. met het maaiveld.


About this dataset:

  • Nauwkeurig inzicht in locaties en oppervlakte groene daken/daktuinen
  • Linked to the BAG and / or address
  • Rekening houdend met omvalling
  • Op basis van openbare luchtfoto of eigen luchtfoto
  • Combinatie met potentie groene daken mogelijk

Tree mapping

Trees have many positive properties and are therefore important objects for municipalities. Trees can also pose risks, such as slipperiness caused by leaves, falling branches or unstable trees. 

It is therefore important to properly map and monitor trees. Urgis maps high-risk trees by looking at the height and volume of trees, and their distance or overhang with roads. This provides insight into risky roads on which maintenance and periodic checks can be adapted. 

About this dataset:

  • Based on 3D elevation model (national open data or local elevation model)
  • Available as vector data with various attributes such as: height, volume, distance to road, overlap with road, terrain openness


Volume and living area of buildings

The living area and volume of a house influences the property value and governmental taxes. When a house is enlarged by a dormer or extension, the surface area and volume will increase. Urgis exactly calculates the volume and living space based on national (or local) 3D data. Existing data can be tested or supplemented with this information.


About this dataset:

  • Based on 3D elevation model (national open data or local elevation model)
  • Linked to the BAG and / or address