Swedish lidar data from Lantmäteriet now offers unprecedented 3D terrain exploration through a new browser-based viewer. The project initially struggled with massive file sizes - a single tile contained 15 million points requiring 200MB storage. By switching to Cloud Optimised Point Cloud (COPC) format, the developer reduced a typical tile's size from 600MB to just 105 MB, achieving near-one-byte-per-point compression. This technical shift enabled loading 194 million points across a 7.5km x 7.5km area.

The tool now visualizes raw lidar categories alongside land cover classifications - explaining the Swedish labels matching source datasets. Users can toggle tree detection algorithms showing both accurate identifications and common errors. A unique layer integrates camera locations from SVT's *Den Stora Älgvandringen* (The Great Moose Migration) television series, revealing how 3D topography influences moose movement choices invisible in traditional 2D broadcasts.

Practical usability improvements include URL-based location sharing, letting users bookmark and distribute specific views. The developer solved hierarchical data loading challenges by restructuring COPC files, though efficiency optimizations remain works in progress. Anil's geospatial research provided crucial architectural guidance for potential nationwide scaling.

This open data initiative transforms inaccessible LIDAR archives into interactive terrain models. By compressing a 2.5km x 2.5km tile from 200MB to functional usability while maintaining scientific fidelity, it sets new standards for public geospatial exploration. The moose migration inspiration proves such tools reveal environmental narratives previously lost in compression artifacts.