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QGIS Planet

1 Million Downloads: QField's Big Milestone

🚀 QField has officially hit 1 million downloads – thanks to you! 🎉

Let’s not beat around the bush: QField has hit 1+ million downloads. What started as an ambitious open-source project has transformed into a global tool that’s changing how professionals collect spatial data in the field. This big milestone is the result of years of dedication, with over 50,000 hours invested by our team. Our GeoNinjas contributed 14% of QGIS, while also driving open-source projects like ModelBaker and SwissLocator.Thank you for making GIS nerds the unsung heroes of fieldwork everywhere. Here’s to changing the world, one field at a time!

🚀 GET QFIELD NOW

From Switzerland to the world!🇨🇭

Born in the Swiss Alps, raised by open-source, and now roaming the globe, QField has gone international! What started in Switzerland is now in the hands of field mappers, researchers, and GIS pros on six continents. Thank you for taking QField worldwide! 🌍

Mapping the world one field at a time.

The numbers tell a story 📖

One million downloads might sound like just a number, but for us, it represents something much bigger. It’s 1'000'000 times someone chose an innovative, flexible mobile mapping solution. It’s 1’000’000 instances of fieldwork made easier, more efficient, and more accurate.

From humble beginnings to over 1 million downloads, QField has officially gone from “little app that could” to “open-source overachiever. ” Thanks to the power of open source (and probably some caffeine).

QField has hit 1 million downloads in over 150 countries.

QField’s top user countries 🏆

QField’s passport is full! 🌍 We’re blown away by how far our geospatial tool has travelled: from mountaintops to city blocks, you’re mapping it all. Our amazing global user community is making QField a true #DigitalPublicGood.A map made in heaven! 💚

Mapping knows no borders, just like QField’s growing community.

More than just an app 📱

This cross-platform flexibility helps professionals collect GIS data anywhere, anytime. QField goes wherever you do. Android? Check. iOS? Check. Desktop? Check. If it has a screen, we’re probably on it. Collect GIS data anywhere, anytime.

QField isn’t just software, it’s a community-driven project that turns complex geospatial challenges into precise, actionable data. Every download represents a connection to our core mission: making professional-grade mobile GIS accessible, reliable, and straightforward.

QField’s Journey: Mapping our milestones 📍

Our roadmap is packed with milestones and highlights that will continue to push the boundaries of mobile GIS.

QField toQFieldCloud ☁️

You can play a key role in the sustainable growth of QField, the open-source digital good. Your support  can take many forms, like contributing… or:

Support option 1
Support option 2
Support option 3

This not only streamlines and enhances your fieldwork but also gives you access to the full QField ecosystem with all its advantages. At the same time, you directly contribute to the continuous improvement of QField, ensuring its impact grows for everyone.

💚 SUPPORT US

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OpenStreetMapとは？QGISでも活用できるオープンデータプロジェクト - QGIS LAB by MIERUNE

はじめに「OpenStreetMap（OSM）」は、街中の自動販売機やゴミ箱からキャンプ場まで様々な種類の地物の情報をオープンデータとして提供しているプロジェクトです。この記事では、OpenStreetMapの特徴と、QGISでの具体的な活用方法について解説します。OpenStreetMapとは？OpenStreetMapとは、誰でも自由に利用できる地図データを作成するプロジェクトです。2004年...
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QGISでCSVやExcelなどの表形式のデータをGISデータに結合する方法 - QGIS LAB by MIERUNE

はじめにQGISでは、GISデータと表形式のデータを組み合わせることで、より深い分析や視覚化が可能になります。この記事では、QGISの「テーブル結合」機能を使って、GISデータに表形式のデータを結合する方法を紹介します。この機能を用いることで、属性テーブルの活用の幅を広げることができます。テーブル結合とは？QGISのテーブル結合機能は、複数の属性テーブルに共通する列の値を用いて、1つのテーブルに情...
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Speed up your analytics with the new MovingPandas 0.22 and Trajectools 2.6

The latest releases of MovingPandas and Trajectools come with many “under the hood” changes that aim to make your movement analytics faster:

  1. Instead of immediately creating a GeoPandas GeoDataFrame and populating the geometry column with Point objects, MovingPandas now has “lazy geometry column creation” that holds off on this operation until / if the geometries are actually needed. This way, for many operations, no geometry objects have to be generated at all.
  2. MovingPandas TrajectorySplitters now support parallel processing and Trajectools uses parallel processing whenever available (e.g. for adding speed & direction metrics, detecting stops, splitting trajectories).
  3. When a minimum length is specified for trajectories, MovingPandas now avoids computing the total trajectory length and, instead, immediately stops once the threshold value has been reached (“early skip”).
  4. Trajectools now offers the option to skip computation of movement metrics (speed & direction). This way, we can skip unnecessary computations and leverage the lazy geometry column creation, wherever applicable.

Let’s have a look at some example performance measurements!

Example 1: MovingPandas ValueChangeSplitter

The ValueChangeSplitter splits trajectories when it detects a value change in the specified column. This is useful, for example, to split up public trajectories that contain a “next_stop” column.

The following graph shows ValueChangeSplitter runtimes for different minimum trajectory length settings (from 0 to 1km, 100km, and 10,000km):

We see that the new, lazy geometry column initialization outperforms the old original code in all cases (e.g. 57% runtime reduction for 1km), except for the worst-case scenario, when the original implementation discards all trajectories as too short right from the start. (For most use cases, min_length will be set to rather small values to avoid creation of undesired short trajectory fragments, similar to sliver polygons in classic geometry operations.)

Additionally, we can engage multiprocessing by setting the n_processes parameter, e.g. to the number of CPUs to achieve further speedup:

Example 2: Trajectools

By applying all above-mentioned speedup techniques, Trajectools is now considerably faster. For example, the following runtime reductions can be achieved by deactivating the “Add movement metrics (speed, direction)” option in the algorithm dialog:

  • Create trajectories: 62%
  • Spatiotemporal generalization (TDTR): 78%
  • Temporal generalization: 81%
  • Split trajectories at stops: 53%

I have also updated the default trajectory points output style. It now uses a graduated renderer to visualize the speed values (if they have been calculated) instead of the previously used data-defined override. This makes the style faster to customize and provides a user-friendly legend:

For more infos, have a look at:

Enjoy the latest performance increases!

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属性テーブルの列の追加と削除〜効率的な方法と注意点〜 - QGIS LAB by MIERUNE

はじめにQGISで扱う地物では、地物に関連する情報を属性テーブルに記録することができますが、作業の途中で属性テーブルの列（フィールド）を増やしたり減らしたりしたい場合があるかもしれません。この記事では、属性テーブルの列の追加・削除の方法、および、注意点について説明します。属性テーブルに列を追加する属性テーブルに列を追加する手順を説明していきます。QGISでレイヤパネルで列を追加したいレイヤを右クリ...
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QGISでジオリファレンスを行う方法〜紙地図・画像を地図に追加する手順〜 - QGIS LAB by MIERUNE

はじめに紙の地図やスキャンした図面などの画像データには位置情報が付与されていないため、GIS上で正しい位置に表示することができません。この問題を解決するのが「ジオリファレンス」という作業です。この記事では、QGISを使って地図画像に座標情報を与える「ジオリファレンス」について、基本的な考え方と具体的な手順を紹介します。ジオリファレンスとはジオリファレンスとは、画像や図面に地理的な位置情報（座標）を...
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GISデータのエクスポート手順〜GeoPackageやCSVに変換しよう〜 - QGIS LAB by MIERUNE

はじめにQGISでは、一時的に作成したレイヤを保存したり、異なるファイル形式に変換するといった機会が多くあります。そんなときに役立つのが、「エクスポート」機能です。この機能を使えば、作業途中のレイヤをファイルとして保存したり、形式を変換して出力したりすることができます。この記事では、「名前を付けて保存」の基本的な使い方と、実際のユースケースを紹介します。エクスポート機能とは「エクスポート」機能は、...
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3D editing tools for Point Clouds

Edit point cloud (LiDAR) data directly in QGIS 3.42 and later. Discover new 3D editing tools, workflows, and demos for efficient point cloud classification.
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プラグインでQGISをもっと便利に〜概要とインストール方法〜 - QGIS LAB by MIERUNE

はじめにQGISは標準機能として多くの解析・編集機能を備えていますが、プラグインを追加することでさらに機能を拡張したり、QGISが標準として持っていない機能を追加することができます。プラグインを活用することで、より効率的なGISデータの作成・編集・解析が可能になります。この記事では、QGISプラグインの概要とインストール方法について解説します。QGISプラグインとは？QGISは、基本機能を持つ本体...
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What’s under the hood of the official QGIS Server Docker image?

The Mysteries of the Official QGIS Server Docker Image
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