Insights

Have you ever wondered whether you can get a clear wireless signal at a specific location? In the real world, the path for that signal isn’t just about distance; it’s about navigating a landscape of trees, buildings, and hills. Traditionally, a technician performed an on-site field test to verify signal coverage. Today, signal coverage predictions can be made using publicly available data combined with user-friendly open-source software tools.

We put together a demonstration project that explores this challenge, showcasing how modern technology can provide instant, accurate line-of-sight analysis for services like fixed wireless internet.

The Underpinnings: From Lidar to Line-of-Sight

The magic behind this system starts with lidar (light detection and ranging), a remote sensing method that uses lasers to create incredibly detailed 3D maps of the Earth’s surface — often with a resolution of just 1 meter. Vast archives of this data are available to the public for free from sources like the USGS’s 3D Elevation Program.

We then process this cloud of points into something more like a 3D map, known as a digital surface model. Unlike a traditional elevation map that only shows the bare ground (a “digital elevation model”), a digital surface model captures the first return of the laser pulse. Think of it as mapping the tops of tree canopies and building rooftops — the very obstacles that can block a wireless signal.

Once we have this detailed 3D model, we can ask a simple but powerful question: from the top of a specific transmitter tower, what can we “see”? The answer reveals the areas of wireless coverage and potential customer locations. This process is called a viewshed analysis. Using open-source GIS libraries like GDAL, we can calculate every point on the map that has a direct, unobstructed line of sight to the tower — giving us a highly accurate coverage map.

Bringing it to Life: The Demo Application

Of course, all this powerful analysis isn’t much use on its own. To make it accessible, we built a simple, user-friendly web application with a serverless backend.

• The frontend is a responsive web application built with vanilla JavaScript. It uses Google Maps JavaScript API for a familiar, interactive map and Google Places API for a seamless address autocomplete search.
• The backend is a lightweight, serverless microservice built on Google Cloud Functions (2nd Gen), which runs on Cloud Run. When a user enters their address, Google Places Autocomplete converts it to latitude/longitude coordinates (geocoding), and the frontend sends those coordinates to the service.
• The microservice then queries a precomputed viewshed map, stored as a Cloud-Optimized GeoTIFF in a Google Cloud Storage Using the Python library Rasterio, it efficiently checks the single pixel value corresponding to the user’s location to determine coverage — returning an instant yes-or-no answer.

Beyond Your Internet Connection: Other Use Cases

While our demo focuses on helping an ISP verify customer coverage, this powerful combination of data and analysis has applications across numerous industries. This technology can aid in the optimal placement of public safety communication towers, 5G cells, and security cameras. It can also be used to plan safe drone flight paths, position environmental sensors, and even provide viewshed analysis for real estate development.

What’s truly exciting is that by combining public data with open-source software, this kind of sophisticated analysis is no longer just for specialized agencies. We can now solve complex, real-world visibility challenges with more accuracy and efficiency than ever before.

Access the source code here: https://github.com/woolpert-digital-innovations/los-coverage-lookup