26 posts
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.
Turn 25 Cloud Optimised GeoTIFFs of Eryri into a static STAC catalogue with pystac and rio-stac, validate it against the JSON schemas, query it, serve it as static files, and compare it with the Earth Search and Planetary Computer STAC APIs through pystac-client.
Process large satellite rasters in Python with dask and rioxarray. Open eight full Sentinel-2 scenes lazily, choose a chunk size, inspect the task graph, watch CPU and memory with dask diagnostics, compare peak memory with eager NumPy, time chunk sizes and thread counts, and write a median NDVI GeoTIFF to disk chunk by chunk.
Create a satellite timelapse in Python from free Sentinel-2 imagery. Select clear scenes with STAC, mask cloud, build one median composite per summer, apply a consistent stretch, add date labels and a scale bar, and export an animated GIF, WebP and MP4 of eleven years of construction at Hinkley Point C.
Run a pixel-wise Mann-Kendall trend test and Theil-Sen slope on 24 years of MODIS NDVI in Python. Read MOD13A1 from the Microsoft Planetary Computer, mask it with the QA layer, aggregate to growing-season means, vectorise the tests with xarray.apply_ufunc (checked against pymannkendall), compare the seasonal and modified Mann-Kendall tests, control the false discovery rate across 229,229 pixels, and map greening and browning on China's Loess Plateau.
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.
Estimate surface soil moisture from free Sentinel-1 radar in Python with the TU Wien change-detection method. Build dry and wet references per orbit, map relative soil moisture at 100 m, and test it honestly against four COSMOS-UK stations and their rain gauges.
Map blanket bog, heath, bracken, grassland, woodland, water and built-up land in the Peak District from 2023 Sentinel-2 seasonal composites with an eight-rule spectral index classifier, then assess it properly: a confusion matrix, user's and producer's accuracy, kappa and its caveats, block-bootstrap confidence intervals, and why a random train/test split overstates accuracy.
Write the classic SAR speckle filters (boxcar, Lee, refined Lee, enhanced Lee, Frost and Gamma-MAP) in NumPy and SciPy, run them on Sentinel-1 over Rutland Water, and compare them by equivalent number of looks, edge preservation and speed.
Read ERA5 climate reanalysis in Python with xarray from free, login-free cloud Zarr stores, clip it to the UK, compute area-weighted means and anomalies against 1991–2020, export a Cloud Optimised GeoTIFF and check it against Met Office stations.
Classify nine upland habitats in the Peak District with LightGBM on 77 multi-temporal features: seasonal Sentinel-2 bands and indices, Sentinel-1 VV and VH backscatter and Copernicus DEM terrain. Tune with spatial-fold cross-validation and class weights, run an S2 / S2+S1 / S2+S1+terrain ablation, explain the model with TreeSHAP and permutation importance, and compare it with a random forest and the Part 1 rules on the same test blocks.
Mask clouds and cloud shadows in Sentinel-2 L2A imagery with Python. Compare the Scene Classification Layer (SCL), s2cloudless, OmniCloudMask and a simple threshold over the Lake District, measure how far they agree, score them against 186 reference points and build a cloud-free median composite.
Map snow cover in Python with the Normalised Difference Snow Index (NDSI) from Sentinel-2 and the daily MODIS snow product on the Microsoft Planetary Computer: choosing the NDSI threshold, separating snow from cloud, snow-covered area through the winter of 2025–26 in the Cairngorms, and the snowline by elevation and aspect with the Copernicus DEM.
Measure shoreline change on one of Europe's fastest-eroding coasts with free Sentinel-2 imagery and Python: MNDWI and Otsu thresholding, sub-pixel shorelines with find_contours, shore-normal transects, a tide correction from open tide-gauge data, end-point and linear regression rates, and an honest comparison with East Riding of Yorkshire Council's cliff monitoring.
Map forest loss and deforestation in Python with the Hansen/UMD Global Forest Change v1.13 data: read the open tiles with no login, compute loss by year in hectares with correct pixel areas, compare Landsat and Sentinel-2 images before and after, and check the GFC loss year against Sentinel-2 NBR and MODIS burned area for central Rondônia, Brazil.
Build a labelled deep learning dataset from free data: stream a Sentinel-2 L2A composite from the Planetary Computer, rasterise Overture building footprints over ESA WorldCover, cut georeferenced image chips, split them by spatial block so the test set does not leak, check the class balance and load it all as a TorchGeo dataset.
Map nitrogen dioxide air pollution from Sentinel-5P TROPOMI Level-2 data in Python: stream a full year of orbits from the Planetary Computer, apply the qa_value > 0.75 filter, grid swaths to a regular raster, build monthly and seasonal composites, rank cities and measure the weekend drop, with an honest explanation of what a tropospheric column is.
Map wildfire burn severity in Python with free Sentinel-2 imagery: NBR calculation, cloud and water masking with SCL, dNBR with an offset, USGS severity classes and burned hectares, a burn perimeter, fire progression, RdNBR and an eight-year recovery record for the 2018 Saddleworth Moor fire.
Monitor crops through cloud with free Sentinel-1 radar. Build VV, VH and VH/VV ratio time series for 247 fields in Cambridgeshire, beat speckle by field-averaging, read the growth signatures of winter wheat and oilseed rape, and compare with the gaps that cloud leaves in Sentinel-2 NDVI.
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.
Supervised land cover classification in Python: build seasonal Sentinel-2 composites with odc-stac, sample labels from ESA WorldCover, train a scikit-learn random forest, test it with spatial cross-validation and map Sheffield and the Peak District.
Build a five-year Sentinel-2 NDVI time series in Python: load a lazy dask datacube with odc-stac, mask cloud with SCL, make monthly composites, extract field curves and derive crop phenology.
Detect ships in free Sentinel-1 SAR imagery with a cell-averaging CFAR detector written in NumPy and SciPy: mask the land, set a threshold from a speckle model, cluster detections into targets, reject wind turbines, sidelobes and ambiguities, compare VV with VH and export the vessels as GeoJSON.
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.
Map the 2022 Sindh floods with free Sentinel-1 radar from the Microsoft Planetary Computer: convert backscatter to decibels, remove speckle with a Lee filter, pick a water threshold with Otsu's method, and measure the flood extent and how it receded over seven months.