In many grid-based problems, you need to choose exactly one column in each row to form a path.But not all columns are equal — some cells are better (think “free road”), others are worse (think “toll road”), and some are blocked. In our example: Our goal: Step 1: Understanding “jumps” A jump happens when you […]
Finding the Best Vertical Path in a Grid (with Jumps and Weights) Read More »
In real-world scenarios like drone delivery, robotics navigation, or sensor error modeling, we often deal with uncertainty. Even if a system is designed to land at a target point, small errors due to wind, GPS inaccuracies, or mechanical drift can cause deviations. To quantify these uncertainties and make predictions, we use probability distributions. Specifically, a
Developing probability distribution function of a given geometry Read More »
Introduction Understanding patterns in accident data is crucial for urban planning, traffic management, and public safety. Clustering is a powerful technique that helps in identifying accident-prone areas by grouping locations with similar characteristics. In this tutorial, we will use Fuzzy C-Means (FCM) clustering to analyze London accident data over 36 months, identifying high-density clusters and
Clustering London Accidents Data Using Fuzzy C-Means Read More »
Introduction Digital Surface Models (DSMs) represent the Earth’s surface, including all features such as buildings, trees, and other objects above the ground. They are widely used in geospatial analysis for applications like urban planning, flood modeling, and vegetation monitoring. By comparing two DSMs from different time periods, we can detect changes in surface elevation caused
How to Compare DSMs in Python: Analyze Surface Elevation Changes and Generate Heat Maps Read More »
In this tutorial, we’ll walk through how to process and save raster chunks in parallel using Xarray and Dask. This technique is particularly useful when working with large raster datasets where chunking and parallel processing can significantly improve efficiency. Prerequisites Before we begin, ensure you have the following Python libraries installed: You can install them
Parallel Processing and Saving Raster Chunks Using Xarray and Dask Read More »
3D tiles are a powerful method for rendering large and complex 3D data sets on the web. They were developed as a solution to the challenges of displaying detailed 3D scenes in a browser, which traditionally required large amounts of data to be transferred and processed. 3D tiles break down the data into smaller, manageable
Rendering Point Cloud in CesiumJS Read More »
Introduction: In this tutorial, we’ll explore how to calculate the azimuth, elevation, and slant range between two points on the Earth’s surface using Python. The concepts of azimuth and elevation are essential in various fields such as navigation, astronomy, and satellite communication. The slant range, on the other hand, is the direct distance between two
In this tutorial, we’ll create a tile service for GeoTIFF files using Python, FastAPI, and various libraries such as xarray, rioxarray, rasterio, and Pillow. This service will dynamically generate map tiles on-the-fly and cache them for efficient retrieval. Prerequisites Before starting, ensure you have the following installed: Overview To get full source code for this
Creating a GeoTIFF raster XYZ tile service in python with caching capability Read More »
Introduction: Routing and pathfinding are essential aspects of geographical analysis, and visualizing network routes can offer valuable insights. In this tutorial, we will explore how to use PyVista and OSMnx to create an interactive visualization of road networks and routes. By the end of this guide, you’ll be able to generate dynamic visualizations that showcase
Network route visualization using pyvista and osmnx Read More »
Introduction: Exploring Digital Elevation Models (DEMs) in 3D with Python Digital Elevation Models (DEMs) are crucial in geospatial analysis, providing a representation of the Earth’s surface topography. These gridded datasets encode elevation information, making them valuable for various applications, including terrain analysis, hydrology, and environmental modeling. In this tutorial, we will delve into the fascinating
Exploring 3D Terrain Visualization with Python: A DEM and PyVista Tutorial Read More »
