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Scalable Sentinel-1 and Sentinel-2 data cubes

Hacker News

Scalable Sentinel-1 and Sentinel-2 data cubes

Download Sentinel-1 & Sentinel-2 data cubes of huge-scale (larger-than-memory) on any machine with integrated cloud detection, snow masking, harmonization, merging, and temporal composites This is a super early-alpha package where I try to provide a single scalable function to users that creates infinitely large Sentinel-1 and Sentinel-2 data cubes in zarr that are as ready for further processing as possible. The routine is super fixed and is built for planetary computer as the data source, the cloudsen12 cloud detection model, and and fmask masked snow coverage detection. The package does just this one thing and hopefully very well. I am immensely grateful for python development tips and or any feedback in general.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, user · Missing: agents, macos, agent
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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