Organization:
Veneto Region - Directorate of Territorial Planning (Region)

Brief description of the Organization launching the Challenge:
Regional authority responsible for spatial planning and territorial governance, with expertise in geospatial data infrastructure, cartography, geographic information systems, and support to territorial development policies.

Region:
Veneto (Italy)

Description of the Challenge:

• The Need:
  Regional and Land Reclamation authorities (Consorzi di Bonifica) face the complex task of managing water distribution across vast agricultural networks during critical scarcity events. Traditional water management relies heavily on localized hydrometric stations and fixed rotation schedules. To ensure a resilient and equitable distribution of water, there is an urgent need for a continuous, regional-scale screening system capable of evaluating water stress and soil moisture dynamics across entire irrigation districts.
• Background Context:
  The Veneto region features a highly intricate network of canals and different agricultural landscapes. When there is scarcity of water, authorities must make macro-level decisions on how to allocate water among different districts and channels. Satellite remote sensing provides the perfect synoptic view to monitor these large areas simultaneously. By integrating in-situ station data and applying AI to regional data streams, the goal is to transform reactive water management into a proactive, data-driven workflow. This will allow management boards to optimize canal flows, balance regional water deficits, and monitor downstream vulnerabilities, such as coastal areas exposed to low-flow impacts.
  
• Specific Objectives:

1. District-Wide Stress Screening: Develop an automated pipeline to aggregate and map crop water stress and soil moisture indices at the scale of irrigation districts and hydrological sub-basins.
2. Macro Water Deficit Analytics: Implement machine learning algorithms to evaluate cumulative water deficits across different regional sectors, ranking irrigation districts by urgency of intervention.
3. System-Wide Allocation Modeling: Train AI models to cross-reference satellite-derived regional stress patterns with canal network structures and river discharge data, simulating the impact of macro allocation choices.
4. Decisional Dashboard: Design a high-level command dashboard for Land reclamation authority and regional policy makers, utilizing a viewer that can highlight districts under critical stress.

• Key Requirements: 
  The solution must operate on a regional scale, leveraging Copernicus Sentinel-2 and IRIDE constellation data, or other Earth Observation Data integrated with regional hydrometric and agrometeorological networks. The outputs must be fully compatible with Regional GIS infrastructures via API/OGC Web Services (WMS/WFS), delivering macroscopic, scannable risk maps tailored for institutional decision-makers.

Your Challenge in just one question:
How can we integrate EO data, AI techniques, and in-situ monitoring to improve water allocation decisions within complex hydraulic networks at the regional scale? 

Topics:

• Earth Observation (EO)
• AI & Data Analytics
• Data-driven Policy Making
• Climate Adaptation & Resilience
• Natural Resources Management (Water, Soil, Forest...) 

Expected role of Service Provider:

• R&D
• Software & Tool Development
• System Integration in the PA platforms
• Consultancy & Feasibility Study