~1
~1m/Pixel EPSG 3857 Austrian LandCover Model
Hacker News~1m/Pixel EPSG 3857 Austrian LandCover Model
Hey HN, we developed a land cover model that works pretty well at least for Austria. Obviously it's not perfect and there is still lots of stuff to improve, but it's important to provide reproducible research artifacts in a world driven more and more by AI-based decision making. This is the first alpha release and the presentation needs some work, but the principal idea has been shown feasible. Stay tuned. Cheers and happy coding.
Share cardActual performance
3points
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: model, coding · Missing: mac, agents, macos86%86% predicted probability of success on Product Hunt, 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 released57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue14%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, crypto4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
Similar products
We
WebGL 1M particles73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
WebGL 1M particles
Hacker News8Dr
Dragon – $1M Kitty65%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Dragon – $1M Kitty
Hacker News1Ma
Manuzoid – Database of More Than 1M Manuals73%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Manuzoid – Database of More Than 1M Manuals
Hacker News234Pi
Pixel Sorter42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Pixel Sorter
Hacker News3Po
Postpone Pixel42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Postpone Pixel
Hacker News2I
I made 1M (!) syntax highlighting themes66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
I made 1M (!) syntax highlighting themes
Hacker News9Si
Simulation of 1M Flocking Birds52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Simulation of 1M Flocking Birds
Hacker News2Ma
Market your product to 1m+ journalists58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Market your product to 1m+ journalists
Hacker News17KT
KTransformers–236B Model and 1M Context LLM Inference on Local Machines67%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
KTransformers–236B Model and 1M Context LLM Inference on Local Machines
Hacker News20Bu
Burnrate $1M a month, backwards through time64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.
Burnrate $1M a month, backwards through time
Hacker News3