StreetComplete, an OpenStreetMap Editor for Humans

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StreetComplete is an OpenStreetMap[0] editor directed at people who want to contribute and want to do this using their smartphone, without learning how to edit things[1]. It is available as an Android application. It is intended to be used as one walks, with quests appearing as markers on the map. Selecting a marker allows one to answer a simple question. The answer will be added to the OpenStreetMap database, with app handling selecting objects for editing, transforming answer into OSM tags and making edits. OpenStreetMap account is needed to apply edits, but it is possible to start without it, make some edits and login/register later. Note: I am not the main author, but I am one of the active contributors. Github page is at https://github.com/westnordost/StreetComplete and https://github.com/streetcomplete/StreetComplete/releases shows what was recently released. [0]OpenStreetMap is a Wikipedia of maps, available on the open licence. This dataset is already used for many interesting or useful projects. And new mappers are always welcomed! [1] JOSM, Vespucci I use them heavily but describing them as newbie friendly would be a blatant lie. iD is more newbie friendly but still is quite complex to use and unusable on mobile. Most of difference is caused that all of them are general purpose editors, for example it is quite hard to make iD even easier to use. I posted https://news.ycombinator.com/item?id=21577029 in 2019, since that time several new quests were added, download become significantly faster and app got much smarter, while hopefully not becoming more confusing.

Visit product View on Hacker NewsDec 21, 2020

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, open · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 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.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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