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Remote AraBle Land Estimation for the support of food security related analyses and decision making

Wroclaw Institute of Spatial Information and Artificial Intelligence
Data Spaces for public administration, Green Deal data space


R-ABLE explores the EU's objective: to ensure food security in the face of climate change and biodiversity loss, with a particular focus on the use of AI and EO data to monitor arable land share. It will provide the platform where the processing of source EO data is performed and sharing the data about arable lands via web services. Users of the services will have access to the data and reports allowing them to monitor arable land presence and dynamics over time to fuel their needs. R-ABLE will enable:

  • easy access - integration of the service with commonly used GIS tools,
  • scalability - satellite data allows the system to obtain information from any selected spatial extent and generate the data,
  • monitoring - repetitive data sets allowing to report changes taking place in time.

Initial coverage: selected regions of Poland.


i-Space Coach


i-Spaces Involved

          The Data Cycle Hub
Main Objectives

R-ABLE aims to deliver data on the current state of arable lands, the dynamics of their share and analytical reports to bridge the gap in data acquisition and exchange between stakeholders at various levels of operations and background.

Main innovations
  • Valuable data of arable land dissemination and its change dynamics at the great scale with the use of the state-of-the-art methodology.
  • Common source of arable land data available to each customer. The data that users need will be brought to them with ease they want, at a cost they can afford.
  • Breakthrough frequency of data update - multiple updates over the single year.

The experiment will result in a spatio-temporal dataset on agricultural lands areas for the selected region in Poland. It will allow the citizens to easily view and monitor the historical and current situation regarding agricultural lands. The data, when analyzed, may be a support for experts and decision makers in taking more conscious actions. The impact, either social or environmental, is located at the decision making and action taking level.

Key Exploitable Results (KERS)
  • KER01: Trained, ready-to-use AI/ML model for detection and segmentation of agricultural land on EO data and the dataset/database fed using the model.
  • KER02: Web services for data access. Standard (WMS/WMTS) and non-standard (reports) web services will be implemented for publication of arable land data.
  • KER03: End user app developed in the form of a plugin for QGIS (Open Source GIS platform) providing access to the web services.
Technical KPIS
  • F1 (F-score) AI/ML model accuracy measure value
  • The system spatio-temporal database fed with N time series of data (data for N moments in time)
  • Web services providing access to the data
  • Assistance to an EUHubs4Data event to present and promote the experiment and the beneficiary company.
  • Contacts with interested entities.


Wroclaw Institute of Spatial Information and Artificial Intelligence (WIZIPISI) was founded in 2012. Our goal is to conduct and commercialize innovative research in the area of GIS, earth observations and AI.