Project - Rooftop Solar Panel Detection using Deep Learning

Name: Project - Rooftop Solar Panel Detection using Deep Learning
Rating: 4.4 (29 reviews)

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

Created bySpartificial Innovations

Last updated 10/2023

English

English [Auto],

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

Course content

3 sections • 16 lectures • 1h 15m total length

Workflow of the Project1:53
Explore the complete workflow for rooftop solar panel detection with deep learning, covering problem statement, dataset analysis and preparation, CNN modeling with stratified k-fold cross validation, and evaluation.
Project Content0:08
Introduction to Project Statement3:05
Understand why detecting rooftop solar panels from aerial imagery matters and how a labeled satellite image dataset enables a solar panel recognition algorithm.
Gist of the Dataset1:29
Provide a dataset overview for rooftop solar panel detection, showing images with panels in varied orientations and backgrounds, and images without panels, with the goal to classify presence.
Importing the Libraries and the Dataset4:12
Import libraries for data handling and convolutional neural network development, and load the rooftop solar panel dataset into Google Colab. Mount Google Drive and set training image and label paths.
Function to prepare data for training and validation9:03
Create a data loader to prepare training and validation data for a rooftop solar CNN by loading images and labels, converting them to NumPy arrays, and pairing IDs with labels.
Analysing and Preprocessing the data9:36
Convert images and labels to NumPy arrays and visualize samples with Matplotlib. Analyze the distribution of solar panel presence and scale pixel values to 0–1 for model readiness.
Quiz 1

Quick Explanation on CNN2:29
Learn how a convolution neural network uses filters to detect solar panels in images, training and cross validating to classify rooftop scenes.
Function to build Convolutional Neural Network (CNN)10:04
Develop a python-based CNN by defining a build_model function using TensorFlow and Keras, stacking convolutional, batch normalization, max pooling, and dense layers, then compiling for training.
Stratified K-Fold Cross Validation to check the model performance7:27
Learn to train and validate a convolutional neural network on your dataset using stratified k-fold cross-validation to address imbalance, compiling train–validation workflow and generating predictions on each validation set.
Building, Training and Assessing the CNN Model4:04
Build, train, and assess a cnn with stratified k-fold cross validation, generate predictions, view the model summary, and interpret accuracy versus the confusion matrix for imbalanced data.
Quiz 2

Evaluation Metrics for Classification (TP, FP, TN, FN)4:02
Compute true positives, false positives, false negatives, and true negatives for binary classification using a confusion matrix and a 0.5 threshold, while examining image IDs and predictions.
Visualising these Evaluation Metrics (TP, FP, TN, FN)6:18
Visualize true positives, true negatives, false positives, and false negatives for rooftop solar panel detection using a show_images function with color cues, enabling binary classification evaluation.
Understanding and Implementing ROC curve and AUC4:45
Explore true positive rate, false positive rate, and the ROC curve, and learn how the area under the curve (AUC) evaluates CNN performance.
Confusion Matrix to evaluate the model's performance3:25
Visualize a confusion matrix to quantify true positives, false positives, true negatives, and false negatives, and compare accuracy with ROC, recall, precision, and F1 score to improve minority-class predictions.
Conclusion of the Project3:46
Celebrate completing the rooftop solar panel detection project by applying CNN-based dataset preparation, stratified k-fold cross-validation, and binary classification metrics to optimize performance; explore Python, data science, and image segmentation.
Quiz 3

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!

Who this course is for:

Whoever interested in Satellite and Aerial image and data science

Project - Rooftop Solar Panel Detection using Deep Learning

What you'll learn

Explore related topics

Course content

Introduction to Project and Data Processing7 lectures • 29min

Introduction to Machine Learning4 lectures • 24min

Evaluation Metrics and Conclusion5 lectures • 22min

Requirements

Description

Who this course is for: