Use the power of Transfer Learning — Table of Contents What is Detectron2? Project Setup Build the Model Training and Evaluation Results Resources What is Detectron2? Detectron2 is an opensource object recognition and segmentation software system that implements state of the art algorithms as part of Facebook AI Research(FAIR). …

Chapter 1: Introduction to PySpark using US Stock Price Data — PySpark is an API of Apache Spark which is an open-source, distributed processing system used for big data processing which was originally developed in Scala programming language at UC Berkely. The Spark has development APIs in Scala, Java, Python, and R, and supports code reuse across multiple workloads — batch…

A detailed explanation of handling satellite imagery in the format of .tiff files using Python. — There is no need to articulate the expanding applications of Satellite and Hyperspectral Imagery which range from our daily necessities such as food and water to space exploration. Thereby we store the said data in different file formats based on utilization such as Tag Image File Format(.tiff), Binary MatLab file…

A detailed explanation and Implementation of the 3D-CNN model for land cover classification of satellite imagery using Python. — There is no need to articulate the importance of satellite imagery analysis in today’s world. The applications of satellite imagery have been crucial to solving a lot of problems not only related to humanity but also the ecosystem. With help of the extensive research in Artificial Intelligence-driven applications, we are…

Part 2: Auto Encoders for land cover classification in Hyperspectral Images using K-Nearest Neighbor Classifier(K-NNC), Support Vector Machine(SVM), and Gradient Boosting using Python. — Table of Contents Quick Recap K-Nearest Neighbor Classifier Support Vector Machine Gradient Boosting Conclusion References Quick Recap A hyperspectral image is an image cube with the shape of R x C x B. where R and C are the image's height and width. B represents the no. of bands in the image.

Land cover classification of Sundarbans satellite imagery using K-Nearest Neighbor(K-NNC), Support Vector Machine (SVM), and Gradient Boosting classification algorithms with Python. — This article helps readers to better understand land cover classification on Sundarbans satellite data using different classification algorithms with Python. Table of Contents Sundarbans Satellite Imagery K-Nearest Neighbor Classifier (K-NNC)

Different methods and Machine Learning techniques to analyze satellite imagery using Python with hands-on tutorials and examples. — This article helps users to understand the different methods and supervised and unsupervised machine learning techniques to analyze satellite imagery using Python with hands-on tutorials and examples. Contents Satellite Imagery Indices Supervised Learning in Satellite Imagery

Embed your interactive plots in Medium, Webpages, and other platforms using Plotly and Chartstudio — There is no such thing as information overload. There is only a bad design. — Edward Tufte We all know a picture is worth a thousand words, data visualization is the visual summary of the information that makes it easier to understand/identify patterns and trends instead of looking at thousands…

A detailed explanation of Data Analysis on Sundarbans Satellite Imagery using Python — This article helps readers to better understand the satellite data and different methods to explore and analyze the Sundarbans satellite data using Python. Table of Contents Sundarbans Satellite Imagery Data Analysis Vegetation and Soil Indices Water Indices Geology Indices Conclusion References Let’s get started ✨