Project - Rooftop Solar Panel Detection using Deep Learning
Harness the Power of Deep Learning to Identify and Analyze Solar Installations from Aerial Imagery
Ratings 4.40 / 5.00
What You Will Learn!
- Complete end-to-end resume worthy project
- Learn about Aerial Imagery and Related Data
- Data Analysis and Preprocessing of Aerial Image data
- Image Machine Learning Algorithms such as CNN
Description
Welcome to "Project - Rooftop Solar Panel Detection using Deep Learning"!
In today's era of renewable energy, solar panels are sprouting on rooftops worldwide. Recognizing them efficiently can empower industries, city planners, and researchers alike. In this hands-on course, we dive deep into the world of artificial intelligence to develop a cutting-edge model capable of detecting solar panels from aerial images.
What you'll learn:
Fundamentals of Deep Learning: Kickstart your journey with a foundational understanding of neural networks, their architectures, and the magic behind their capabilities.
Data Preparation: Learn how to source, cleanse, and prepare aerial imagery datasets suitable for training deep learning models.
Model Building: Delve into the practicalities of building, training, and fine-tuning Convolutional Neural Networks (CNNs) for precise detection tasks.
Evaluation and Optimization: Master techniques to evaluate your model's performance and optimize it for better accuracy.
Real-World Application: By the end of this course, you will have a deployable model to identify rooftop solar installations from a bird's-eye view.
Whether you're a student, a professional, or an enthusiast in the renewable energy or AI sector, this course is designed to equip you with the skills to contribute to a greener and more technologically advanced future. No previous deep learning experience required, though a basic understanding of Python programming will be helpful.
Harness the synergy of AI and renewable energy and propel your skills to the forefront of innovation. Enroll now and embark on a journey of impactful learning!
Who Should Attend!
- Whoever interested in Satellite and Aerial image and data science