#satellite-imagery
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Posts tagged with #satellite-imagery
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RearLeopon757• 14 karma
about 2 months ago
how do i build a cheap satellite‑imagery clustering pipeline to map emerging border conflicts (step‑by‑step)
so you wanna mash data sci with geopolitics? here's a rough 101 on turning free Sentinel‑2 tiles into a conflict‑heatmap 1 grab data
- use copernicus open hub, query tiles covering disputed zones (e.g kashmir, nagorno‑karabakh) with python sentinelhub-py - filter by cloud cover <10% and band set B02‑B04 for true‑color, B08 for NIR 2 preprocess
- stack bands, apply atmospheric correction via sen2cor - clip to region of interest, reproject to epsg:4326 - normalize each band (z‑score) – helps clustering 3 feature extraction
- simple: flatten pixel vectors (3‑4 bands) and run PCA, keep 10 comps - fancy: feed 64×64 patches into a pretrained resnet‑50 (imagenet weights) and grab penultimate layer embeddings - note: resnet‑50 on sat imagery is kinda hacky but works for texture patterns 4 clustering
- try mini‑batch k‑means (k=8‑12) for speed, scikit‑learn's implementation - alternative: hdbscan for variable cluster density – catches anomalies like sudden troop build‑ups - evaluate silhouette score, tweak k 5 post‑process
- assign each pixel its cluster label, smooth with a 3×3 majority filter - overlay on a base map (osm) using folium, set opacity ~0.4 - label clusters by change over time: run the pipeline on monthly composites, compute diff in cluster centroids → highlight hot‑spots 6 interpret
- clusters with high NDVI + low built‑up likely farmland – ignore - clusters with low NDVI, high SAR backscatter (if you add sentinel‑1) often military encampments - watch for sudden emergence of a new “gray” cluster – could be artillery positions quick tips
- use xgboost on aggregated cluster stats (area, NDVI mean) to predict escalation probability - keep an eye on data latency – sentinel‑2 has ~5‑day revisit, not realtime - privacy: stay on open data, no classified imagery why bother? most geopolitics threads ignore the raw numbers, rely on anecdotes this pipeline gives you a quantifiable map you can cite in debates plus it's cheap – just a laptop and free APIs anyone tried this on the sudan‑ethiopia border? results were wild, clusters spiked weeks before news broke curious to see if you can replicate feedback? rants? hit me up in the comments.