9 posts
Turn the LOCA group of a real AGS4 file into points in British National Grid, see what happens when the CRS is wrong, summarise each hole's geology from GEOL, check every ground level against Environment Agency LIDAR, and build a QGIS project with labels and a map tip that shows the borehole log.
Build a printable model of central Durham from OpenStreetMap footprints and LiDAR heights. Give every building a roof height, or shape the roofs from the surface model, remove detail too thin to print, and prove the city and its two-colour parts are watertight.
Turn a GPX track into a printable terrain model: the Pennine Way from Edale to Crowden, draped on Environment Agency LiDAR, with the route raised, engraved, or printed as a separate colour inlay. Every STL is proved watertight before it is written.
Make a 3D terrain map in Python with PyVista. Turn an Environment Agency LIDAR DEM into a StructuredGrid, drape a Sentinel-2 image on it, compare vertical exaggeration and lighting, drape a GPX route over Striding Edge, and render off-screen to PNG and a rotating MP4 on Windows, macOS or Linux.
Build Voronoi (Thiessen) polygons in Python with shapely and GeoPandas, clipped to a real boundary. Compute Thiessen-weighted catchment rainfall for the River Eden from Environment Agency rain gauge readings, check the gauges, compare with the gauge average and IDW, then compare Voronoi 'nearest fire station' areas in Cumbria with drive-time service areas from OpenStreetMap.
Compute height above nearest drainage (HAND) from the Environment Agency LIDAR DTM at 5 m with pysheds, turn it into inundation extents for a range of stage heights on the River Severn between Shrewsbury and Buildwas, relate them to the Buildwas gauge, and score them honestly against the Environment Agency Flood Map for Planning with the hit rate, false-alarm ratio and critical success index.
Estimate rooftop solar potential in Python for 2,148 buildings in Manchester: take a 1 m Environment Agency LiDAR DSM and Overture Maps footprints, compute roof slope and aspect, trace horizon angles for shading from neighbours and trees, build a sun map from a PVGIS typical year with pvlib, check it against pvlib's Perez model and PVGIS, then estimate kWp and kWh per year per roof and rank the best roofs.
Run a viewshed analysis in Python on a real DEM: compute what one wind turbine can see with gdal.ViewshedGenerate, rebuild the same line-of-sight algorithm in numpy, correct for earth curvature and refraction, build a cumulative zone of theoretical visibility (ZTV) from 26 turbines, draw line-of-sight profiles, and measure how much trees and buildings in a LiDAR DSM shrink a bare-earth viewshed.
Read the open Meta/WRI global 1 m canopy height map from AWS with no login, compare it with ETH Global Canopy Height 2020, and validate both against a canopy height model made from Environment Agency LiDAR in the Forest of Dean: CRS alignment, bias, RMSE, hexbin plots, height classes and honest limitations.