Patch the Planet
- Machine Learning
Patch the Planet - Restore Missing Data
The aim of the project is to create an algorithm that accurately fills in the missing data volume based on the available contexts.
2024 - present
Python, Pytorch, Lightning
Geophysical
Web application + Machine Learning
About the Project
"Patch the Planet" is the first part of the Encoded Reality challenge series, consisting of four parts that tackle different approaches to the analysis and modeling of geophysical data. The aim of this project is to develop an algorithm capable of accurately completing missing data volumes based on local context. This project is an implementation initiative for the Science Club, enabling students to practically apply their knowledge in the field of artificial intelligence.
Team
Our team consists of machine learning specialists and programmers who have joined forces to tackle the challenges of the "Patch the Planet" project. Our algorithm experts possess deep knowledge in data modeling, including machine learning and deep learning techniques. Familiarity with machine learning algorithms, such as neural networks, clustering algorithms, and classification, enables us to develop effective strategies for analyzing and filling in missing data. Additionally, our ability to optimize algorithms and solve computational efficiency issues allows us to achieve high precision and performance in our solutions. Our strength lies in collaboration and open exchange of ideas, which allows us to effectively grow and achieve our goals.
Jakub Chojnacki
AI Engineer
Julia Farganus
AI Engineer
Daniel Borkowski
AI Engineer