24 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.
Five new free tools for herders, district offices and cooperatives: a pasture and water bulletin, a water point check, a weekly fire count, flood water near a village today and a rainfall insurance check. Real answers from Kenya and South Sudan.
An introduction to the TechnicalGIS climate time-series tools: five open datasets, how to draw an area on the map, worked examples from London, north Evia, Nairobi and Las Vegas, the processing limits and the terms for reusing every output.
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.
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.
Segment 2023 Sentinel-2 seasonal composites of the Peak District into image objects with scikit-image SLIC and Felzenszwalb, tune the segment size against Living England boundaries, compute per-object statistics, shape and GLCM texture with NumPy, classify the objects with LightGBM and compare OBIA with a pixel-based classifier on accuracy, salt-and-pepper noise and patch counts. Then export the habitat objects as GeoPackage polygons.
Classify habitats from a full year of cloudy, irregular Sentinel-2 observations with two temporal neural networks in PyTorch: a 1D convolutional TempCNN and a lightweight temporal attention encoder (L-TAE) with day-of-year positional encoding. Compare masking with gap-filling, test against a LightGBM baseline on spatial blocks, read which dates matter from attention and occlusion, and predict a wall-to-wall 10 m map on a CPU.
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.
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.
Train a U-Net to segment Sentinel-2 imagery into buildings, built-up land, trees, grass and water with PyTorch, segmentation_models_pytorch and TorchGeo's Sentinel-2 pretrained ResNet-18 weights, on a CPU in five minutes. Real loss curves, per-class IoU and F1, a confusion matrix and an honest look at what limits accuracy.
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 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.
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.
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.
Search seven summers of Sentinel-2 imagery with pystac-client, stream only the bands you need from Cloud Optimised GeoTIFFs, mask clouds, compute NDWI and MNDWI and measure how much of Lake Mead dried up between 2019 and 2025.
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.