Space-to-Soil: Practical AI Applications in Satellite Imagery

To provide participants with the skills to preprocess satellite imagery, build predictive models for weather forecasting and climate monitoring, and apply AI for decision-making in agriculture and urban planning.

• Understand the types of satellite data (remote sensing, multispectral, hyperspectral imagery) and their applications in space science.
• Learn AI techniques for preprocessing satellite imagery (filtering, normalization, feature extraction) and preparing data for machine learning models.
• Build predictive weather models using satellite data, AI, and machine learning techniques (regression, neural networks, LSTMs).
• Monitor climate patterns using satellite data to predict future climate trends.
• Apply AI tools to satellite data for agricultural applications (crop health, land-use analysis) and urban planning (urban growth, environmental management).
• Develop a decision-support tool for interpreting satellite data and making informed decisions in agriculture and urban planning.

• Researchers, data scientists, environmental engineers, urban planners, and professionals interested in AI applications in satellite data interpretation.

• Basic knowledge of Python and machine learning is helpful, but not required.

• Participants should have an interest in utilizing satellite data for climate, agriculture, or urban planning applications.

• Preprocess and analyze satellite imagery using Python (OpenCV, scikit-image) for AI-based interpretation.
• Build and evaluate predictive models for weather forecasting and climate monitoring using machine learning techniques.
• Develop AI applications for satellite data interpretation in agriculture (precision farming, crop health monitoring) and urban planning (land-use analysis, environmental management).
• Create a decision-support tool that uses satellite data to provide actionable insights for policy and planning in agriculture and urban development.