3 posts
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.
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.
Invert Sentinel-1 InSAR for a volcanic source in Python: write Mogi point-source and Okada (1985) sill forward models in NumPy, downsample ascending and descending LOS maps with a quadtree, fit depth and volume change by weighted least squares with scipy.optimize, and estimate uncertainties with a Monte Carlo of correlated noise, compared with published results.