10 posts
Mosaic four Copernicus DEM GLO-30 tiles straight from AWS with rasterio, reproject them to British National Grid, write a validated Cloud Optimised GeoTIFF with rio-cogeo, and measure exactly how many bytes HTTP range requests fetch.
Make a kernel density heatmap from points in Python: compare Scott/Silverman, likelihood cross-validation and a fixed planning distance, Gaussian and Epanechnikov kernels and sample weights, export an edge-corrected GeoTIFF with rasterio, show it on a basemap, and map case-control relative risk on Camden's London plane trees.
Assess a real land cover map in Python: ESA WorldCover 2021 against 8,597 Eurostat LUCAS 2022 field survey points in Ireland. Build the confusion matrix, compute overall, user's and producer's accuracy, see why kappa is discouraged, and use the Olofsson et al. (2014) stratified estimators to get error-adjusted areas with 95% confidence intervals and to plan a new stratified random sample.
Detect and quantify methane plumes in Python with NASA's EMIT imaging spectrometer: load open EMIT methane enhancement data from NASA VEDA without a login, separate three Dallas-Fort Worth landfill plumes from the noise, compute the integrated mass enhancement, estimate emission rates with ERA5 winds, and compare them with EPA greenhouse gas inventory figures.
Convert raster to vector in Python with rasterio.features.shapes on 10 m ESA WorldCover for Greater Manchester: polygonize water and built-up land, fix invalid polygons, simplify without gaps, rasterize ONS district boundaries for land-cover shares, then write and validate a Cloud Optimized GeoTIFF with rio-cogeo and measure HTTP range reads.
A practical guide to coordinate reference systems in Python with pyproj, GeoPandas and rasterio, tested on real Greater Manchester data. Covers EPSG codes, WGS 84 vs British National Grid (EPSG:27700), the always_xy axis-order trap, OSTN15 vs Helmert datum shifts checked against 13,187 NaPTAN bus stops, measured area and distance errors in the wrong CRS, and reprojecting a GeoDataFrame and a Copernicus DEM raster.
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.
Calculate zonal statistics in Python with exactextract and rasterstats on real data: ONS 2021 LSOA boundaries for Cumbria with GHSL population, the Copernicus DEM and ESA WorldCover. Covers partial-pixel weighting vs pixel centroids, CRS alignment, categorical land-cover fractions, a speed benchmark, GeoPackage export and choropleth maps.
Run a trained PyTorch U-Net over a whole Sentinel-2 scene of a city it has never seen: tiled sliding-window inference on a large GeoTIFF with overlapping, blended windows, a georeferenced Cloud Optimised GeoTIFF of classes and confidence, raster-to-vector polygons with rasterio.features.shapes in a GeoPackage, and an honest check against reference labels.
Read only the pixels you need from a cloud-hosted Copernicus DEM, reproject to British National Grid, derive hillshade, slope and aspect with NumPy, compute zonal statistics and draw an elevation profile — all in one script.