This project addresses critical global environmental issues, such as rapid urbanization and natural resource depletion, by utilizing high-resolution, multi-spectral satellite imagery from Sentinel-2, part of the European Union's Copernicus program. By applying advanced machine learning algorithms, we aim to predict future changes in land cover, including the area and density of vegetation, water bodies, and urban regions. These predictions are intended to support sustainable development goals by providing actionable insights for environmental management and urban planning.
- Environmental Volatility: Significant and rapid changes in water, vegetation, and urban landscapes pose sustainability challenges.
- Manual Processing Limitations: Traditional manual processing of satellite data is slow and often inaccurate, limiting the effectiveness of analyses.
- Predictive Model Shortcomings: Existing models do not fully leverage the potential of high-resolution satellite imagery.
- Demand for Advanced Tools: There is a crucial need for automated, precise predictive tools for proactive and informed environmental planning.
- Urgency of Environmental Monitoring: Immediate and accurate monitoring is essential for effective resource management.
- Impact on Policy and Planning: Insights from this project can significantly enhance decision-making for land use and resource management.
- Scientific and Methodological Contribution: Integrating machine learning with satellite imagery enhances environmental prediction methods.
- Supporting Sustainable Development: The project’s outputs help balance human impact with ecological preservation.
- Source: Copernicus Open Access Hub
- Provider: European Space Agency's Copernicus Program
- Satellite: Sentinel-2
- Size: 45.6 GB
- Coverage: High-resolution multi-spectral imagery data
- Temporal Coverage: January 2019 to December 2023, monthly images (excluding June to September each year due to cloud coverage)
- Data Usage: Analyze trends in water bodies, vegetation, and urban expansion
- Models Implemented: Linear Regression, Random Forest, Gradient Boosting, SVM, and Neural Networks
- Key Metrics: RMSE, MAE, and R^2 values across various predictions
- Handling Missing Values: Missing data points were handled using interpolation techniques to maintain data integrity.
- Effective Monitoring: High-resolution imagery enabled detailed observations of environmental changes.
- Advanced Analytical Techniques: Sophisticated machine learning algorithms provided accurate predictions of future environmental conditions.
- Insights for Policy Making: The project's findings are invaluable for formulating strategies to address urgent environmental challenges.
This project demonstrates the potential of integrating satellite imagery with advanced machine learning techniques to monitor and predict environmental changes on a global scale. The insights generated are crucial for sustainable development and proactive environmental management.