Introduction: In this blog, we’ll walk through the process of using Python to classify land cover from satellite imagery using Convolutional Neural Networks (CNNs). We’ll use the freely available EuroSAT dataset, which contains Sentinel-2 satellite images covering 13 spectral bands and 10 different land cover classes. A Convolutional Neural Network…

Introduction Artificial Neural Networks (ANNs) have made significant strides in the field of computer science, enabling computers to understand and process information in complex ways. As we continue to explore the vast expanse of outer space, ANNs have found an essential role in enhancing satellite technology, offering unprecedented insights into…

Traditional Water Quality Analysis Water quality is a critical parameter in the health of our planet’s ecosystems and the biodiversity they sustain. It refers to the chemical, physical, and biological characteristics of water, primarily in relation to human consumption. The biosphere — comprised of all life on Earth and her…

In this tutorial, we’ll explore the concept of hydrodynamic modeling, using Python’s powerful suite of numerical and scientific libraries. Hydrodynamic modeling is a key tool in understanding water bodies and their behavior under different conditions. Setting Up Your Environment Before we start, make sure to have the following Python libraries installed: pip install numpy pip…

In this tutorial, we will deep-dive into the realm of satellite-based atmospheric and ionospheric anomaly detection using Python. This is an advanced tutorial, and the reader is expected to be familiar with Python programming, the basics of satellite data, and some knowledge of atmospheric and ionospheric science. Setting up the Environment To start with…

This tutorial aims to demonstrate how distributed data processing frameworks, like Hadoop’s MapReduce and Apache Flink, can be leveraged for the parallel computation and high-velocity analytics of voluminous environmental data such as multispectral satellite imageries or high-frequency climate sensor data. Setting Up the Environment To begin with, you need to set up your environment…

Introduction In our previous article we discussed the potential of Python to enhance the analysis of remote sensing data. We provided an example using the MODIS dataset to demonstrate how Python can simplify various tasks including data import, basic operations, and supervised classification. Now we will explore two additional applications of…

Introduction Python has become a popular tool for processing and analyzing remote sensing data due to its simplicity, versatility, and robust package ecosystem. With libraries such as rasterio for reading and writing raster data and scikit-learn for machine learning, Python has made it simpler than ever to analyze remote sensing data. …

Google Earth Engine (GEE) is a powerful cloud-based platform for planetary-scale geospatial analysis. It combines a multi-petabyte catalog of satellite imagery and geospatial datasets with powerful analysis capabilities. The Supervised Classification process in remote sensing involves training a classifier to recognize patterns in data that correspond to known classes of…