Aerial photo analysis
Besides providing various services we are also fully engaged in developing relevant and qualitative data products. Among other things, we use artificial intelligence (AI) that we apply to aerial photos with which objects can be recognized on a large scale quickly and with high accuracy. Urgis has developed a number of algorithms with which we can quickly obtain information from (spatial) data.
Are you interested in one of our analyzes, or would you like more information about the applications of AI on geographic data? Then take contact us.
Solar energy inventory
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 number, and in particular the surface, of solar panels already present is a very important information layer. To help municipalities with its energy transition strategy, Urgis has developed a deep learning algorithm (AI) that segments solar panels with very high accuracy. The result is combined with height data to calculate effective panel areas. As a municipality, you have immediate insight into the number of solar panels at address, neighborhood or district level! If you are interested in this product, or if you would like to participate in collective purchasing, please contact us.
About this analysis:
- Visual overview of solar panels (vector) in your entire municipality.
- Direct insight into net panel surfaces per address, neighborhood, district or building function through link with BGT, BAG or CBS data.
- Reliable result of at least 96% detected surface!
- Linked to address by tilt correction
- 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.
Solar energy potential on roofs
Which roofs are suitable for solar panels? How much energy can these solar panels potentially yield? Using a 3D analysis, it is possible to accurately determine how much solar energy ends up on a surface all year round. This will make shadows visible through, for example, trees and dormers. We automatically segment individual roof sections per building, which provides insight into the potential energy yield per roof section. With this information you gain insight into how much solar energy can potentially be generated on 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 if available) or a more recent municipal elevation model can be used.
About this analysis:
- 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
- Solar energy per individual roof section
- Irradiation at address level by linking to BAG (average irradiance, suitable roof surface, etc.)
- Available as vector or raster
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 in 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
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
We can automatically recognize all objects that are visible from the sky on aerial photos. We apply artificial intelligence (AI) to remote sensing images (aerial photos, drone images, satellite images, etc.). Depending on the object and the resolution of the images, this can be done on a large scale with very high accuracy.
Curious about the possibilities? Take no strings attached contact us for more information!