Show HN: Algorithmically finding the longest line of sight on Earth

Project and Core Idea

• Site precomputes “longest line of sight” for points on Earth using global DEM data, then visualizes them as heatmaps and individual lines.
• Focus is on terrain-scale visibility (mountains, valleys), not local obstructions like buildings or trees.
• Several related tools are referenced that compute per-point viewsheds or panoramas, but this project emphasizes “global exhaustive search” and performance (Rust, SIMD).

Atmosphere vs. Theoretical Lines of Sight

• Multiple comments stress that real visibility is often far shorter due to haze, humidity, dust, and lighting.
• Long-distance record photographs (≈480 km, ≈440+ km) required extreme planning, ideal weather, and favorable lighting (often just before sunrise).
• Some note strong refraction effects (e.g. Föhn over the Alps) both improving and distorting apparent distance; others question what counts as a “picture” when objects are silhouettes.
• Authors say the algorithm includes a standard refraction coefficient and they’d like to explore extreme-refraction cases in future runs.

Data, Resolution, and Reliability

• Underlying DEM is 3 arcseconds (100 m) global data (viewfinderpanoramas), so buildings, vegetation, and fine terrain are smoothed out.
• This leads to obviously wrong claims in dense cities or back gardens; defenders argue it’s intended for large-scale topography, not street-level accuracy.
• Higher-resolution LiDAR exists (even centimeter-scale for some cities) but would explode storage/compute requirements.
• Artifacts are visible, e.g. grid-like patterns in flat Florida terrain from DEM cleaning.

Algorithmic Choices and Discrepancies

• Tool rotates terrain around each observer and scans a 1° azimuth “band of sight,” trading off accuracy for tractable global computation.
• Developers report viewshed area errors typically around 0.5–2% due to rasterization, interpolation, and limited angle coverage, distinct from projection errors.
• Another long-sightline researcher points out a ∼7 km discrepancy on the claimed world record line; both sides agree they’re likely sampling slightly different coordinates and not “casting enough rays.”
• North-face Himalayan views and some Colombian peak labels/coordinates are suspected to be off, highlighting sensitivity to DEM and sampling.

Feature Requests and Use Cases

• Strong demand for photos, 3D relief views, and automatic Google Earth/panorama links to “complete the story.”
• Requests for: top N longest lines from a point; approximate visibility in all directions (per-direction maxima); or coarse “visible area” rings. Full per-direction storage for every point is noted as potentially petabyte-scale.
• Proposed and actual uses include: ham radio and microwave QSOs, Meshtastic/LoRa mesh planning, WiFi experiments, SOTA-style peak activations, long-distance hiking goals, geology/geomorphology visualization, and “finding all of something” (e.g. cycling climbs).
• Some see it as a good anti–flat earth demonstration; creators even muse about running the model on a hypothetical flat Earth for fun.