SuPerWorld Geo-API: Artificial intelligence in real estate for modelling risks

Description: The SuPerWorld geospatial application programming interface (Geo-API) consists of various geospatial and geo-analytics services which offer rich contextual information related to real estate properties, including environmental risks and risks related to climate change. This Geo-API brings innovative digital solutions to insurance and financial sectors operating in real estate. It supports automated, highly-accurate property evaluations, which have the potential to revolutionize the industry by reducing time and human cost on property evaluations, considering potential risks coming from natural and/or man-made disasters.
The API combines large-scale earth observation technologies and Artificial Intelligence (AI), allowing to valorize the real estate market more completely, more effectively, more reliably and more realistically, considering the impact of environmental risks and climatic changes on properties. The API enables comparisons among different locations and may be used to identify trends and patterns, both locally and regionally, incorporating land cover mapping and land use changes through time. Most of the API's services, at the second version of the API allow to select also polygons instead of single points.


  1. v1.0: Created during our collaboration with WIRE-FS company (now obsolete, outdated and deactivated).
  2. v2.0: More advanced, new services, better accuracy on existing services, accepting both points and polygons as input. This version of the SuPerWorld Geo-API has been used for the development of the Gaea Geospatial tool.

The services offered by the SuPerWorld Geo-API v2.0 are provided for the geographical area of the Republic of Cyprus and are the following:

  1. Land use around the property (agricultural, urban, rural, green areas, forests, etc.).
  2. Changes in land use (e.g. construction started, construction completed, etc.). Ability to potentially provide number of houses/ units, number of buildings, etc. This is particularly important in tracking how real estate stock, especially residential, is changing in a given area.
  3. Area of a building (or a number of buildings if a polygon has been selected)
  4. Ranking of building quality as can be considered from its roof.
  5. Detection of swimming pools inside the property (or a number of swimming pools if a polygon has been selected).
  6. Detection of the area and the volume of the property.
  7. Identify presence, density of vegetation & type (trees, wild, crops etc.) surrounding the property.
  8. Number of trees identified inside some geographical area.
  9. Risk score for landslide susceptibility near the property.
  10. Risk score for land subsidence and displacement on the property.
  11. Risk score for flooding on the property.
  12. Risk score for earthquake damage on the property.
  13. Wildfire risk at the property.
  14. Whether the property falls inside a previously burnt area (after a wildfire disaster).
  15. Ground slope and aspect (direction) of the property.
  16. Geology and soil type at the property.
  17. Precipitation averages where the property is located.
  18. Wind speed averages where the property is located.
  19. Elevation of the property from sea level.
  20. Proximity of the property to the nearest road, including dirty roads.
  21. Proximity of the property to the nearest existing electricity network.
  22. Proximity of the property to the sea.
  23. Proximity of the property to beaches marked with the blue flag award.
  24. Proximity of the property to amenities (schools, hospitals, supermarkets, etc.).
  25. Whether the property falls under Natura 2000 protected area.

Asfa Jamil, Chirag Padubidri, Savvas Karatsiolis, Indrajit Kalita, Aytac Guley and Andreas Kamilaris,
GAEA - A Country-Scale Geospatial Environmental Modelling Tool: Towards a Digital Twin for Real Estate, Proceedings of the 37th edition of Environmental Informatics (EnviroInfo 2023), Munich, Germany, October 2023.