GreenTrack

Here is the interface screenshot

Inspiration

The increasing concerns about deforestation and environmental degradation inspired us to create this project. With the rapid loss of forest cover globally, we wanted to contribute by providing a data-driven approach to monitor and analyze the health of our planet's green spaces.

What it does

GreenTrack uses satellite imagery and machine learning to analyze and visualize greenery, deforested, and forested areas. It provides insights into environmental changes over time, helping users monitor deforestation and reforestation efforts.

How we built it

We gathered satellite images of different regions, labeled areas based on their greenery level (forested, deforested, or green-covered), and trained a deep learning model for image classification. The results are visualized on an interactive map, allowing users to explore environmental changes in a user-friendly interface.

Challenges we ran into

Data Quality: Satellite images with varying resolutions made it challenging to get consistent results. Model Accuracy: Achieving high accuracy in classifying images required fine-tuning and data preprocessing. Processing Power: Handling large-scale data for real-time processing required optimizing the model and ensuring scalable infrastructure.

Accomplishments that we're proud of

Successful integration of satellite imagery with AI to classify environmental data. Real-time data visualization on an interactive map. A user-friendly interface that provides meaningful insights on global greenery changes.

What we learned

We learned how satellite imagery and machine learning can be used together to analyze environmental trends. We also gained experience in data preprocessing, model training, and deploying a scalable solution.

What's next for GreenTrack

Next, we plan to: Expand the dataset to include more regions and types of imagery. Improve the model's accuracy by integrating more advanced algorithms. Implement real-time updates and further refine the map interface for better user engagement.