trAI

Problem Statement: The conflict in Ukraine has led to widespread destruction of infrastructure. Transparency in reconstruction efforts is vital to ensure that resources are allocated efficiently and rebuild trust in public services. Current challenges include identifying damaged structures, assessing the level of damage, and estimating the costs for repair and reconstruction, all while combating potential corruption.

Proposed Solution: Our application leverages advanced AI algorithms to analyze satellite data for detecting and assessing damaged buildings. By quantifying the extent of damage and categorizing buildings by type and size, we can estimate repair costs more accurately. This process ensures a data-driven approach to prioritize reconstruction efforts, providing a transparent and accountable framework to international donors and local authorities.

Innovation and Impact: The application introduces a high level of automation in damage assessment which traditionally is manual, time-consuming, and subject to human error. The potential impact includes speeding up the recovery process, optimizing the use of funds, and restoring essential services to communities more efficiently.

Target Audience/Beneficiaries: The primary beneficiaries are the Ukrainian public and government, humanitarian agencies, and international donors involved in reconstruction efforts.

Implementation Plan: The plan involves satellite data acquisition, AI model training, pilot testing in select regions, and scaling the solution nationwide. Partnerships with local authorities and international organizations will be crucial.

EU Space Technologies

Data Utilization 
We will use high-resolution satellite imagery from EU space programs such as Copernicus to monitor and analyze infrastructure damage. This imagery provides extensive coverage and frequent updates essential for tracking reconstruction progress.

Value Proposition
EU space technologies offer unparalleled data quality and reliability, which is crucial for accurate damage assessment. This helps in reducing the time needed for on-ground surveys and makes the reconstruction process more efficient and less susceptible to corruption.

Technical Application
The application will process satellite data using machine learning to identify structural damage levels. It will then integrate this data with GIS to create a comprehensive mapping solution that visualizes the scope of damage and reconstruction needs.

Advantages Over Current Methods
The use of EU space technologies enables a more rapid and large-scale response compared to traditional survey methods. It provides a cost-effective solution that is critical in times of limited resources and immediate need.

Marcin: analitics and technical lead.

Krzysztof: prezentation and esthetic lead.

Malgorzata: frontend developer and technical.

Michal: research and assitant : )