Cityfier ESA Feasibility Study
Research project investigates how satellite data on green areas could benefit city planning.
Using satellite data to facilitate land-use planning
AINS Group aims to find out how data on green areas transmitted by satellites could be utilised in city planning through a research project that receives funding from the European Space Agency (ESA). The objective is to develop the calculation model in the existing land-use planning tool so as to consider green areas and waterways in addition to traffic connections and the accessibility of private and public services.
Data transmitted by Sentinel-2 satellite will be utilised in the research project. Additionally, Very High Resolution (VHR) images will be obtained from an image bank that purchases images from various commercial satellites. In practice, the project analyses the connection that various values from nature show with housing prices and their development in a particular area by using green-area data.
The aim in the initial stage is to create a general-level machine-learning model that maps between price information and green-area data. Two clients are involved in the pilot project: The Master Planning Unit of the Helsinki City Planning De-partment and Espoo’s City Planning Department. VTT is working as a research partner for AINS Group
Cityfier analyses and forecasts the value of a city district by combining different data sources as input data. The following input data is used:
- GIS Data which includes the information about the residential properties and the proximity of private and public services (shops, schools, health care, transportation)
- Information about housing prices
- Information about green areas and water bodies extracted from earth observation data
- Urban master plans for the area in order to know how the district is going to develop in the future
As a result Cityfier offers the current valuation and a forecast of the value increase of the current district. The modelling includes assessment of how job and population numbers may develop, the scheduling of investment projects, a value measurement based on urban economics data, and forecasting of an area’s increase in property values. There is also a possibility to compare different plans and choose the one which has the best value increase. The analysis results and other vital KPIs can be visualized using Cityfier and delivered to the customers as online reports.
The objective of this study is to prove the feasibility of introducing new input data sources (= earth observation data) into Cityfier by evaluating their predictive power in the applied context and their cost impact to the service or product prices. The proposed approach combines information about green areas and water bodies extracted from earth observation data, housing prices and the proximity of private and public services and analyzes them using machine learning.
The current status of the research project can be viewed from the Project Web Page hosted by ESA