Project - Rooftop Solar Panel Detection Using Deep Learning

Harness the Power of Deep Learning to Identify and Analyze Solar Installations from Aerial Imagery

What you'll 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

Requirements

Python Programming Basic Knowledge is Required

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!

Overview

Section 1: Introduction to Project and Data Processing

Lecture 1 Workflow of the Project

Lecture 2 Project Content

Lecture 3 Introduction to Project Statement

Lecture 4 Gist of the Dataset

Lecture 5 Importing the Libraries and the Dataset

Lecture 6 Function to prepare data for training and validation

Lecture 7 Analysing and Preprocessing the data

Section 2: Introduction to Machine Learning

Lecture 8 Quick Explanation on CNN

Lecture 9 Function to build Convolutional Neural Network (CNN)

Lecture 10 Stratified K-Fold Cross Validation to check the model performance

Lecture 11 Building, Training and Assessing the CNN Model

Section 3: Evaluation Metrics and Conclusion

Lecture 12 Evaluation Metrics for Classification (TP, FP, TN, FN)

Lecture 13 Visualising these Evaluation Metrics (TP, FP, TN, FN)

Lecture 14 Understanding and Implementing ROC curve and AUC

Lecture 15 Confusion Matrix to evaluate the model's performance

Lecture 16 Conclusion of the Project

Whoever interested in Satellite and Aerial image and data science