There are always new and exciting terrains to discover in the field of geospatial data: from practical applications that help us better understand physical topography and social infrastructures, to theoretical approaches that allow us to navigate abstract spaces .
It's been a while since we covered this topic in Variable. So we're excited to share a selection of recent articles this week that offer a fascinating insight into working across the wide range of use cases that geospatial data encompasses. From beginner-friendly tutorials to more advanced theoretical questions, we're sure you'll find plenty here to pique your interest, whatever your background and experience level.
- Explore location data using a hexagonal grid
By leveraging versatile data from the Helsinki City Bike Program, Sara Tähtinen provides a detailed introduction to the power of Uber's global H3 hexagonal grid system, which is both “a user-friendly and practical tool for spatial data analysis” and a practical method “for anonymizing location data by aggregating geographical information in hexagonal regions”. - Depth Anything – A basic model for monocular depth estimation
In a carefully explained paper presentation, Sacha Kirch unveils the complexities of monocular depth estimation, “the prediction of distance in 3D space from a 2D image” – a problem that requires practitioners to apply their geospatial, computer vision and digital skills. deep learning, and which a new basic model aims to solve.
- Reduce the scale of a satellite thermal image from 1000 m to 10 m (Python)
There are many ways to generate powerful environmental information based on satellite images, but working with this type of data comes with its own set of challenges. Mahyar Aboutalebi, Ph.D. publishes frequently around this subject; one of its latest tutorials focuses on a Python-based approach to scaling down thermal imagery captured by the Sentinel-2 and Sentinel-3 satellites. - How to find yourself in a digital world
Curious about the ever-changing world of robotics? Eden B.The first TDS article focused on the question of robots' ability to self-localize, a crucial requirement for many common products (think: self-driving cars and delivery robots); their article then describes how we can use probabilistic tools to calculate location. - Where is EU Horizon H2020 funding going?
Geospatial analysis can be a useful first step in answering questions that go well beyond geography. Concrete example : Milan Janosov's new tutorial, which brings together data analysis, network science and a healthy dose of Python to map thousands of EU-funded research and innovation projects.