Project - Rooftop Solar Panel Detection using Deep Learning

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Spartificial Innovations

1:15:41

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1 - Introduction to Project and Data Processing

1 - Workflow of the Project.mp4

01:54

2 - Project Content.html

3 - Introduction to Project Statement.mp4

03:05

4 - Gist of the Dataset.mp4

01:29

5 - Importing the Libraries and the Dataset.mp4

04:12

6 - Function to prepare data for training and validation.mp4

09:03

7 - Analysing and Preprocessing the data.mp4

09:36

2 - Introduction to Machine Learning

8 - Quick Explanation on CNN.mp4

02:29

9 - Function to build Convolutional Neural Network CNN.mp4

10:04

10 - Stratified KFold Cross Validation to check the model performance.mp4

07:28

11 - Building Training and Assessing the CNN Model.mp4

04:04

3 - Evaluation Metrics and Conclusion

12 - Evaluation Metrics for Classification TP FP TN FN.mp4

04:03

13 - Visualising these Evaluation Metrics TP FP TN FN.mp4

06:18

14 - Understanding and Implementing ROC curve and AUC.mp4

04:45

15 - Confusion Matrix to evaluate the models performance.mp4

03:25

16 - Conclusion of the Project.mp4

03:46

Description

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

Who is this for?

Whoever interested in Satellite and Aerial image and data science

What You Need to Know?

Python Programming Basic Knowledge is Required

More details

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 this course is for:
Whoever interested in Satellite and Aerial image and data science

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Spartificial is a platform started in April 2021 for Engineering and Science students to learn and work on projects that are solving urgent problems in real world. It is important for a student to learn with an intention for overall technical, social and mental development. We are fan of Project-based learning because that's what life is and in our professional life, our adult life, we are not judged by our test scores or grades but the quality of our character and the quality of our work.

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