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
  To meet the targets of European and national regulations, the Veneto Region has set an ambitious goal for new installed renewable capacity by 2030. To govern this transition, the regional government is implementing a new Plan (Piano di individuazione delle Zone di Accelerazione Terrestri) to identify areas dedicated to fast-tracked renewable energy deployment. In this framework, public administrations currently lack a spatially explicit inventory of small and medium-sized solar installations (such as residential and industrial rooftops). To effectively plan acceleration zones while protecting agricultural land, a scalable GeoAI tool is needed to map and monitor this hidden solar footprint across the entire regional territory.
• Background Context
  The Veneto region features a complex mosaic of land uses, characterized by widespread artisan and industrial districts mixed with residential areas and highly productive agricultural plains. Photovoltaic systems are rapidly expanding across rooftops of all sizes. Because large-scale ground plants are already documented, regional planners specifically need to detect and classify small-to-medium installations. The challenge is to leverage GeoAI techniques on high-resolution imagery to automatically extract these widespread rooftop solar footprints, transforming raw imagery into a structured geographic dataset to fill the current information gap.
• Specific Objectives:

1. Automated PV Detection & Segmentation: Develop a GeoAI pipeline (using deep learning models) to automatically detect and outline solar panel installations from high-resolution imagery.
2. Installation Typology Classification: Implement machine learning algorithms to classify the detected systems into distinct categories: for example, ground-mounted (agrivoltaic/utility-scale), industrial rooftop, etc.;
3. Solar Density & Capacity Analytics: Calculate regional-scale statistics, aggregating solar panel surface area and estimating installed capacity at the municipality and district levels to identify saturated vs. high-potential zones, as well as to guarantee compliance with current legal requirements.
4. Planning Support Dashboard: Design an intuitive web dashboard for spatial planners, enabling both analysis and ongoing monitoring.

• Key Requirements: 
  The solution must leverage high-resolution open data (such as aerial orthophotos available), or other high resolution imagery integrated with Copernicus Sentinel-2 and especially IRIDE constellations data for continuous monitoring of new large-scale installations. The output must be fully compatible with the Regional Spatial Data Infrastructure (via WMS/WFS and API) to ensure seamless integration into existing GIS workflows and decision-making processes.

Your Challenge in just one question:
How can we combine remote sensing imagery using GeoAI to properly map solar installations and monitor the regional renewable energy acceleration zones?

Topics:

• Earth Observation (EO)
• AI & Data Analytics
• Energy
• Critical Infrastructures 

Expected role of Service Provider:

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