Challenge Description: Join the "GreenGuard" Challenge to combat deforestation using AI and satellite imagery. Develop innovative solutions to detect, analyze, and predict deforestation events, aiding in the protection of our planet.

Why Deforestation Matters: Deforestation contributes to climate change, biodiversity loss, and indigenous displacement. Leveraging AI and satellite imagery can enhance monitoring and prevention efforts.

Approaches:

1. Detect Current Forest Coverage: Use satellite imagery to map forested regions and distinguish between types of vegetation.
2. Analyze Historical Data: Implement time-series analysis to detect deforestation events by identifying anomalies in historically forested areas.

Goal: Develop an alert and action system that not only detects potential deforestation but also provides actionable insights for conservation efforts and policymaking.

Criteria for Winning:

• Accuracy and efficiency in detecting forest coverage and assessing health.
• Effectiveness in predicting deforestation and differentiating between forested and non-forested areas.
• Innovation, scalability, and potential impact on conservation and policymaking efforts.

Recommended Data Sources:

• Eurosat Dataset for classification.
• DeepGlobe Dataset for segmentation.
• Amazon and Atlantic Forest image datasets for semantic segmentation.
• Amazon Rainforest dataset for semantic segmentation.
• Sentinel-1 for Science Amazonas for time series prediction and anomaly detection.

Additional Resources: Explore OpenForest, a data catalog for machine learning in forest monitoring.

This summary encapsulates the "GreenGuard" challenge, emphasizing its goals, approaches, evaluation criteria, and recommended data sources. It serves as a comprehensive guide for participants aiming to combat deforestation through innovative solutions.