13 posts
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.
Batch geocode all 192 open schools in Sheffield in Python with geopy and OpenStreetMap Nominatim (1 request per second, a custom user agent and an on-disk cache) and with postcodes.io bulk postcode lookups, reverse geocode a sample, and measure every method honestly against the Department for Education's own coordinates. Covers free text against structured queries, failure modes, cross-checking two geocoders, and the licensing of geocoded results.
Build a walking cost surface from real slope (Copernicus GLO-30) and land cover (ESA WorldCover), compute the accumulated cost surface and the least-cost path with scikit-image's MCP_Geometric, add a direction-aware Dijkstra version with SciPy, and compare the routes with the straight line and the OpenStreetMap footpaths from Pen-y-Pass to the summit of Yr Wyddfa (Snowdon).
Map earthquake damage from free Sentinel-1 radar in Python. Compute the log ratio and a z-score against a pre-event stack for Antakya after the February 2023 Türkiye earthquakes, aggregate to a 200 m grid, and score the map against 730 destroyed buildings mapped in OpenStreetMap.
A practical guide to spatial joins in GeoPandas with real Greater Manchester data: 13,187 NaPTAN bus stops, 1,386 OpenStreetMap schools, 215 ONS wards and 1,702 LSOAs. Covers point in polygon with sjoin, predicates, counts per polygon, how='left'/'inner'/'right', sjoin_nearest with max_distance, clip and overlay, and a timing comparison of a naive loop against the spatial index.
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.
Turn classified LiDAR into building footprints with heights in Python: stream USGS 3DEP COPC tiles, build a DTM and a building nDSM from ASPRS class 6 points, polygonise and regularise the mask, split roofs at height steps, compute height statistics per building, score the result against OpenStreetMap with IoU, and export a GeoPackage and a 3D view.
Run Meta's Segment Anything Model on high-resolution aerial imagery with segment-geospatial (samgeo), on a CPU. Generate masks automatically, vectorise them to a GeoPackage, filter them by area, shape and NDVI to find buildings, and prompt SAM with points and boxes, checked against OpenStreetMap.
Calculate land surface temperature from Landsat 8 and 9 Collection 2 Level-2 data in Python: ST_B10 scale and offset, QA_PIXEL cloud masking, LST by land cover and NDVI, an urban-rural transect, hotspots with OpenStreetMap and an honest comparison with air temperature, for London in the July 2022 heatwave.
Track chlorophyll, turbidity and peat colour proxies in four Peak District reservoirs with free Sentinel-2 imagery: cloud and water masking for small, dark upland lakes, NDCI and NDTI time series from 2019 to 2026, anomaly ranking, maps, tidy CSVs and an honest check against Environment Agency samples.
Download Sheffield's walkable street network from OpenStreetMap with OSMnx, find the shortest walk across the city, draw 5–20 minute isochrones and map how far every street is from the nearest supermarket.
Add OpenStreetMap, satellite and Google basemaps to QGIS 3.40 LTR, 3.44 and 4.x as XYZ Tiles connections: the correct tile URLs, the OpenStreetMap tile usage policy, the licence-safe ways to get Google, Esri and Sentinel-2 imagery, and the same basemaps from PyQGIS, folium, leafmap and contextily.