Organization:
Arpae Emilia-Romagna - Regional Agency for Prevention, Environment and Energy (Regional Agency)

Brief description of the Organization launching the Challenge:
Arpae is the Regional Agency for Prevention, Environment and Energy of Emilia-Romagna, established by regional law in 2015. The Agency integrates environmental monitoring, inspection, and control duties with administrative responsibilities for environmental permitting, concessions, and energy regulations.
Arpae focuses heavily on prevention, supporting sustainable development and implementing predictive models to better understand anthropogenic and natural impacts on ecosystems. Additionally, Arpae plays a key role in meteorological and climatological observation, forecasting, and research to support climate change adaptation and mitigation policies, alongside environmental health studies.
Arpae's mission is to ensure environmental safety, health protection, and sustainability through knowledge and resource enhancement. It operates as part of the Italian National System for Environmental Protection (SNPA).

Region:
Emilia-Romagna (Italy)

Description of the Challenge:
The Need: 
Arpae requires accurate, frequent, and high-resolution spatial data to monitor soil consumption and track year-over-year variations across the entire Emilia-Romagna region, overcoming the limitations of current visual and manual processes
Background Context: 
Currently, soil consumption monitoring is carried out on an annual basis. However, because high-resolution AGEA orthophotos are only available every three years, the assessment in the intermediate years is inevitably more approximate. Purchasing commercial high-resolution imagery annually for the entire region is financially unsustainable for the Agency. This lack of consistent, high-resolution annual data is the main bottleneck preventing the development and implementation of an automated or semi-automated methodology to detect year-over-year variations. Consequently, the process remains heavily dependent on the time-consuming visual comparison and manual screening of changes conducted by a team of 10 operators.

Specific Objectives:

• [PRIORITY] Develop an automated or semi-automated methodology that cross-references Earth Observation (EO) data with datasets and a wide variety of local administrative layers and registries (such as CTR, quarries, landfills, and active production sites) to ensure a high-resolution, consistent annual monitoring of soil consumption, soil sealing, and renaturalization.
• Identify, evaluate, and select which available local datasets and thematic layers are most suitable and reliable for this cross-referencing process.

Distinguish with high accuracy between permanent artificial surfaces and temporary or agricultural modifications, strictly adhering to the highly detailed and standardized ISPRA classification system for soil consumption.
As a secondary operational improvement, enhance the generation and accuracy of change-detection masks to reduce false positives and better guide the operators' final visual verification.
Key Requirements: 
The solution must deliver an automated or semi-automated system capable of smartly integrating Copernicus Data Space Ecosystem data with updated local territorial databases. Since local data sources are varied and heterogeneous (ranging from cartography like CTR to specific environmental and industrial registries), a core requirement of the challenge is defining an automated methodology to ingest and prioritize these local inputs when updated. The final outputs must provide high-precision detection of soil consumption changes, be fully compatible with the Agency's internal workflows and GIS platforms, and be specifically designed to automate the data-crunching phase to streamline the operators' validation.
Additionally, the system architecture must be designed to be highly sustainable in terms of computational resources and processing power, ensuring long-term operational efficiency. 

Your Challenge in just one question: 
How can we leverage high-resolution satellite data and artificial intelligence to automatically and continuously detect soil consumption for regional environmental monitoring?

Topics:

• Earth Observation (EO)
• AI & Data Analytics
• Digital Transformation of PA
• Data-driven Policy Making
• Natural Resources Management (Water, Soil, Forest...)
• Smart Cities & Urban Planning 

Expected role of Service Provider:

• Software & Tool Development
• Data Supply & Processing
• System Integration in the PA platforms 

Additional Input: (se presente)
For regional context and current data trends on land cover, see Arpae's dedicated section on soil consumption in Emilia-Romagna