A beginner's step-by-step guide to start using Docker Containers for Data Science development and avoid complex Python environment managers. — Introduction Docker containers provide a lightweight and efficient way to package and deploy applications, making it easier to move them between different environments, such as development, testing, and production. However, while Docker is widely used for deployment, it has been underutilized by developers for their day-to-day work. Many developers still rely…

Discover the advantages of using DevContainers and Codespaces for seamless geospatial development across platforms and devices — Introduction In one of my latest stories, published here on TDS (Configuring a Minimal Docker Image for Spatial Analysis with Python), I demonstrated how to configure a Docker image for Python with essential geospatial analysis tools such as GDAL and XArray. Managing package dependencies can be challenging, particularly when working with…

A step-by-step guide for those wanting to learn geospatial data analysis with Python — Introduction Why is this topic relevant? It’s no secret that data science careers are in high demand these days. And when you add the dimension of geospatial analysis to the mix, the possibilities become even more exciting. Climate change, food production, and transitioning to a carbon-free economy are just a few…

Learn how to install the basic geospatial dependencies, such as GDAL and XArray, and deploy them as a container — Introduction Newcomers to the Python programming language quickly understand the significance of utilizing virtual environments and package management tools. The vast number of packages available presents a challenge in maintaining compatibility among dependencies, making virtual environments and package management critical components of a well-organized Python environment.

Assessing the true power of Apple Silicon architecture for geospatial analysis. — Introduction When I started working with geospatial data science during my PhD in 2019, I knew that the volume of data would require significant computational power. Those who work with geospatial data know that it’s easy to fill up memory by loading satellite imagery into Numpy arrays and combining them into…

An end-to-end deep learning geospatial segmentation project using Pytorch and TorchGeo packages — Introduction In the previous stories (Part 1 and Part 2), we saw how to prepare a dataset of raster (multispectral) images and combined them with the corresponding labels (ground truth masks) using the IntersectionDataset provided by TorchGeo. …

An end-to-end deep learning geospatial segmentation project using Pytorch and TorchGeo packages — Introduction In the previous story (Part 1 — here), we saw how to create a RasterDataset using TorchGeo and a RandomSampler to draw patches from it. In the current story, we are moving further and we will concatenate both the images and the masks into a IntersectionDataset, to draw tuples with…

An end-to-end deep learning geospatial segmentation project using Pytorch and TorchGeo packages — Introduction According to its documentation, TorchGeo is a “PyTorch domain library providing datasets, samplers, transforms, and pre-trained models specific to geospatial data”. The cause is noble. Make it easier for practitioners to use Deep Learning models on geospatial data. And why is that a good deal?

Step-by-step guide on how to automatically test your Python code on every change. — Introduction I’ve been using PyCharm as my main development IDE for a long time, and think its integration is still the best for Python programming. The major drawback, in my opinion, is the fact that Jupyter support is not available in the community edition. As I explained in my previous story…