Photo by NASA on Unsplash

Introduction

When I started using Google Earth Engine (GEE) back in 2018, I found it an amazing idea to have all the satellite imagery datasets in the cloud, and accessing them in such on-demand basis. Once we understand that the computations happen on the server side, through the Earth Engine API…

Photo by Elisa Ventur on Unsplash

Introduction

It’s no doubt that the launch of the project Jupyter, and its notebooks, back in 2015, changed the relation between scientific programmers and their code. The first reason is the simplicity to connect to different programming languages (kernels) and combine text with code snippets and outputs such as tables and…

Photo by Markus Spiske on Unsplash

Introduction

It’s been some time I wanted to write about this topic, but I must confess I’ve been procrastinating it a little bit. Although I don’t have a formal Computer Science background myself (I have a civil engineer degree), I’ve been working with the IT industry for more than 15 years…

Thoughts and Theory

Photo by Harry Quan on Unsplash

Introduction

As technology evolves rapidly, remote sensing is finding its many uses across different subjects, from forest fire mapping to water quality assessment, 3D surface modeling and many others. The advantages of obtaining physical characteristics of an area by measuring its reflected and emitted radiation at a distance are many. However…

Photo by Austin Distel on Unsplash

Introduction

Hi! Today we will continue to develop the Mean Reverting algorithm introduced in the last post Implementing a Simple Mean Reverting Pairs Trading Algorithm in the Quantconnect platform (Part 1) [1], and hopefully improve its results .

The idea behind the linear mean regression presented in Part 1 is that…

Photo by Austin Distel on Unsplash

Update

For information about the course Introduction to Python for Scientists (available on YouTube) and other articles like this, please visit my website cordmaur.carrd.co.

Introduction

Hi! In my last story “Understanding and Implementing Kalman Filter for Pairs Trading” [1] I’ve used an example from the book Algorithmic Trading: Winning Strategies and Their…

Photo by Chris Liverani on Unsplash

For information about the course Introduction to Python for Scientists (available on YouTube) and other articles like this, please visit my website cordmaur.carrd.co.

Introduction

Kalman filter, despite its name, is a two step (prediction and correction) estimator algorithm. Kalman filter is most used in tracking and control systems to provide accurate…

Photo by Savvas Kalimeris on Unsplash

Introduction

Optical Remote Sensing analysis depends on understanding the processes of absorption and scattering of solar radiance on ground objects. If we measure the solar incidence radiance and the surface’s irradiance, we will be able to estimate surface’s reflectance. Reflectance in various wavelengths is the key to understand the target we…

Introduction

Hi, and welcome back to part 6 of Python for Geosciences. One advantage of using a programming language like Python to perform satellite image analysis instead of a geospatial software is that we can automate any part of the process. This automation can be used in a production chain to…

Introduction

Welcome back for the 5th part of this series. On the previous Python for Geosciences post (here), we learned how to work with bit masks provided by satellite imagery, specifically for the case of the Landsat 8. It is very handful to mask undesired pixels or to select specific targets…

Maurício Cordeiro

Geospatial/Financial Data Scientist. Doctorate student @ Université Paul Sabatier (Toulouse). To know more access: http://cordmaur.carrd.co .

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store