There is always new and exciting terrain to discover in the field of geospatial data: from practical applications that help us better understand physical topography and social infrastructure, to theoretical approaches that allow us to navigate abstract spaces.
It's been a while since we covered this topic on Variable, so this week we're delighted to share a selection of recent articles that offer fascinating insights into work across the wide range of use cases that geospatial data encompasses. From beginner tutorials to more advanced theory questions, we're sure you'll find plenty here to pique your interest, regardless of your background and experience level.
- Exploring location data using a hexagonal grid
Leveraging versatile data from Helsinki's urban bike program, Sara Tahtinen provides a detailed introduction to the power of Uber's H3 global hexagonal grid system, which is both “a practical, easy-to-use tool for spatial data analysis” and a practical method “for anonymizing location data by aggregating geographic information.” to hexagonal regions”. - Depth Anything: a basic model for monocular depth estimation
In a thoroughly explained paper tutorial, Sasha Kirch discusses 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 deep learning skills, and that a new basic model aims to solve.
- Downscaling a thermal satellite image from 1000 m to 10 m (Python)
There are numerous ways to generate powerful environmental insights based on satellite imagery, but working with this type of data comes with its own challenges. Mahyar Aboutalebi, Ph.D. publishes frequently on this topic; One of his latest tutorials focuses on a Python-based approach to downscaling thermal images captured by the Sentinel-2 and Sentinel-3 satellites.. - How to find yourself in a digital world
Are you curious about the constantly evolving world of robotics? Eden B.TDS's debut paper focused on the question of robots' ability to self-localize, a crucial requirement for many common products (think: self-driving cars and delivery robots); His post goes on to describe how we can use probabilistic tools to calculate location. - Where do EU Horizon H2020 funds go?
Geospatial analysis can be a useful first step on the path to answering questions that go far beyond geography. Case in point: Milan JanosovThe new tutorial, which brings together data analysis, network science and a good dose of Python to map thousands of EU-funded research and innovation projects.
