Jupyter Blog - JupyterGIS

JupyterGIS 0.16: New visualization capabilities, collaborative Story Maps, and more

2026-08-24T16:09:00+00:00

Read this article in Notebook.link, as a live story-map! https://notebook.link/@martinRenou/jupytergis-announcement

Earlier this year, we introduced STAC browsing and Story Maps in JupyterGIS, making it easier to discover geospatial datasets and communicate results without leaving Jupyter.

JupyterGIS 0.16 continues in the same direction. This release adds support for new geospatial formats, tighter integration with the scientific Python ecosystem, a redesigned Story Map editor, and a more expressive way to style geographic data.

Story Maps are getting a new look!

Story Maps in JupyterGIS let you build a scrollable presentation around your map. A Story Map is made up of a sequence of segments that can combine Markdown content with map views, so you can guide the reader through a geographic story as they scroll.

Each segment can define its own map state, including the current map location, visible layers, and layer styling. This means that the map can change as the reader moves through the story: layers can appear or disappear, the view can move to a new location, and symbology can change to highlight different aspects of the data.

Story Maps have received a significant update in this release.

Read this article in Notebook.link, as a live story-map! https://notebook.link/@martinRenou/jupytergis-announcement

The Story-map associated to this release annoucement. It contains Text, images, and map views with associated layer states.

A better Story Maps editing experience

We’ve also made substantial improvements to the Story Map editing experience.

The editor has been redesigned around Jupyter’s real-time collaboration infrastructure, allowing multiple people to edit the same Story Map simultaneously. Changes appear immediately for everyone, making it much easier to prepare presentations, reports, or educational material as a team.

Collaboratively edit the Story Map markdown.

The editor now gives you a much better sense of what the final story will look like while you are working on it. Markdown sections can be previewed directly in the editor, and a new Story Map preview makes it possible to see the complete presentation without leaving the editing workflow. This makes it easier to write, arrange, and refine a story while keeping an eye on the final result. We’ve also introduced a new layout that is better suited for long-form content. In addition to guided geographic narratives, Story Maps can now be used to create richer articles combining text, maps, images, and other interactive content.

New Story Map editor: Set story segment viewport, preview markdown, set layers properties for the story segment.

OpenEO layers

More and more geospatial workflows rely on remote processing instead of downloading datasets locally. openEO provides a common API to describe these processing pipelines as process graphs that are executed by a backend.

JupyterGIS can now display openEO process graphs directly as map layers. Instead of exporting intermediate results before visualizing them, you can connect an openEO backend and inspect the output of your processing pipeline directly in the map.

The visualization is tile-based and lazy: JupyterGIS only requests the data needed for the current map view and zoom level. This makes it possible to explore large remote sensing workflows interactively, without materializing the full result locally.

JupyterGIS can make use of any openEO server that supports tiling, such as titiler-openeo

import openeo
from jupytergis import GISDocument
from openeo.processes import process

connection = openeo.connect(SERVER_URL)
connection.authenticate_basic(username=BASIC_AUTH, password=BASIC_AUTH)

cube = connection.load_collection(
    "sentinel-2-global-mosaics",
    bands=["B03", "B08"],
    temporal_extent=["2022-04-15", "2022-12-31"],
)

cube = cube.reduce_dimension(
    dimension="t",
    reducer="first",
)

cube = cube / 10000.0

# NDWI = (GREEN - NIR) / (GREEN + NIR)
ndwi = cube.ndvi(nir="0", red="1")

ndwi_vis = (ndwi + 1) / 2

ndwi_png = ndwi_vis.linear_scale_range(
    input_min=0,
    input_max=1,
    output_min=0,
    output_max=255,
)

result = ndwi_png.save_result(format="PNG")

doc = GISDocument(latitude=40.75, longitude=-73.95, zoom=12)
await doc.ready()
doc.add_openeo_tile_layer(result)
doc

Define a process graph to compute a NDWI, using the Python API of OpenEO and JupyterGIS. It is then lazily evaluated on a per-tile basis while the user pans/zooms on the map.

In addition to defining openEO process graphs from the scripting Python API, JupyterGIS provides an advanced openEO process graph editor, allowing you to:

connect to an openEO tile server
define the graph graphically, with boxes and arrows
load data collections and define processes with a drag-and-drop UI
directly edit the JSON content

Editing an openEO process graph from the JupyterGIS front-end

Another interesting aspect of openEO is that process graphs have a well-defined, declarative JSON representation. Because of this structured format, they are a natural target for LLM-assisted workflows. Users can describe the analysis they want in natural language, have an LLM generate or refine the corresponding process graph (e.g. using jupyterlite-ai), and immediately visualize the result in JupyterGIS. Combined with the lazy, tile-based rendering, this makes it possible to quickly iterate on processing pipelines without waiting for complete datasets to be exported or downloaded.

Lazy visualization of Xarray datasets with jupyter-tiler

JupyterGIS now integrates with the new jupyter-tiler package, making it straightforward to visualize Xarray datasets from Python.

Datasets can come from anywhere: they may already exist in your notebook, or they can be loaded on demand from a STAC catalog using stackstac. Once you have an Xarray object, JupyterGIS can display it in the map without requiring an export to another format.

Rendering happens lazily, generating only the tiles needed for the current view. This makes it possible to explore datasets that are much larger than memory while keeping navigation responsive.

The result is a smoother workflow from data loading, to analysis, to visualization, all within the same notebook.

await doc.add_data_array_layer(
    name="NDSI Layer",
    data_array=ndsi,
    colormap_name="viridis",
    colormap_range=(-1, 1),
)

Visualizing an Xarray dataset in JupyterGIS.

This feature requires the optional dependency jupyter-tiler to be installed.

pip install jupyter-tiler

A more expressive symbology model

Styling geographic data often requires combining multiple visual properties to communicate patterns effectively.

JupyterGIS 0.16 introduces a new symbology model inspired by the Grammar of Graphics. Instead of relying on a fixed set of styling options, visual properties such as color, size, and opacity can be defined in a more flexible and composable way.

This makes it easier to build everything from simple thematic maps to more advanced visualizations while keeping styling definitions consistent and reproducible.

A symbology example: apply a Viridis color map to the circle colors, a linear scale to the radius of circles, and a fixed stroke color.

GeoZarr and GeoPackage support

This release also expands the range of formats that JupyterGIS can open directly.

Support for GeoZarr makes it possible to work with cloud-native multidimensional geospatial datasets, while GeoPackage support improves interoperability with existing GIS software and common data exchange workflows.

New Collaborative Editing Capabilities

JupyterGIS 0.16 also brings collaborative editing to vector layers. When working on a shared JupyterGIS document, multiple users can now edit the same vector data at the same time.

Features can be created, moved, and edited collaboratively, with changes synchronized in real time between users. This makes it possible to work together on tasks such as digitizing features, annotating a map, or refining a dataset without having to exchange files or manually merge changes.

Combined with the collaborative Story Map editor, this makes collaboration a more integral part of JupyterGIS: users can work together on the data itself, and then use the same shared document to explore and communicate their results.

A new R API

JupyterGIS 0.16 also introduces an R client, bringing JupyterGIS to R users through the new r-jupytergis package. The R client provides bindings for interacting with JupyterGIS widgets from an R notebook, using the same JavaScript front-end as the Python client.

The main interface is the GISDocument widget, which can be used to create and manipulate JupyterGIS documents directly from R. This makes it possible to build geospatial workflows in R while using the same interactive map interface available to Python users.

The R client also uses the same underlying collaborative infrastructure as the Python client, including the Yrs CRDT library. This means that R users can participate in the same collaborative JupyterGIS workflows rather than working in a separate environment.

doc <- GISDocument$new("france_hiking.jGIS")
layer <- doc$add_raster_layer(
    url = "https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}",
    name = "Google Satellite",
    attribution = "Google",
    opacity = 0.6
)

Bug fixes and performance improvements

As usual, this release also includes many smaller improvements throughout the project.

We’ve fixed a number of bugs, improved performance in several parts of the application, and continued polishing both the user interface and the Python API.

JupyterGIS continues to evolve as a collaborative GIS environment that fits naturally within the Jupyter ecosystem. Whether your workflow starts from a notebook, a STAC catalog, an openEO backend, or a local dataset, the goal remains the same: make it easier to move between analysis, visualization, and communication without switching tools.

Acknowledgements

This work on JupyterGIS by QuantStack was funded by the European Space Agency (ESA) for the Story-maps development, the R API, openEO layers support and the collaborative labelling. Additionally, QuantStack was funded by the French National Centre for Space Studies (CNES) for the lazy visualization of xarray datasets.

Contributors to this release

By order of number of contributions:

Arjun Verma is a Developer at QuantStack. He worked on the server-side geoprocessing infrastructure and on the openEO editor in the JupyterGIS front-end.

Martin Renou is a Technical Director at QuantStack and a maintainer of JupyterGIS. For this release, Martin coordinated and guided much of the development, and worked on the integration of openEO layers.

Gregory Mooney is a Developer at QuantStack and a long-time contributor to JupyterGIS. He led much of the work on the new Story Map editor and its collaborative editing capabilities.

Matthias Meschede is Chief Operating Officer at QuantStack. He introduced the new Grammar of Graphics-inspired symbology model, bringing a more expressive and composable approach to styling geographic data.

Matt Fisher is the Community Manager of GeoJupyter. He contributed to many of the discussions around the release and helped shape several of the design decisions across the project.

Nakul Verma is an open-source contributor to JupyterGIS. He contributed numerous bug fixes and improvements throughout the release, and introduced support for Vega expressions in the new symbology system.

Antoine Prouvost is a Developer at QuantStack. He led the initial work on the R API for JupyterGIS, building its first skeleton and establishing the foundations for the r-jupytergis package.

Sandrine Pataut is a Developer at QuantStack. She worked on bringing many of JupyterGIS’s features to the R API, helping make the new client more complete and useful for R users.

We are grateful to everyone who contributed code, reviews, ideas, discussions, and feedback to this release. JupyterGIS continues to benefit from an increasingly diverse community of contributors, and we look forward to seeing what comes next!

Expanding Geospatial Workflows in JupyterGIS: STAC Browsing and Story Maps

2026-02-19T17:34:00+00:00

JupyterGIS story maps in action

Since its initial announcement less than two years ago, JupyterGIS has been a growing effort to bring interactive geospatial workflows into the Jupyter ecosystem. Early milestones focused on laying the foundations: a composable GIS interface in Jupyter, a shared document model allowing real-time collaboration, a Python scripting API for exploration in the Jupyter Notebook, and a compatibility layer with the QGIS file format.

With the project maturing, recent work has focused on improving two key aspects of everyday geospatial workflows: discovering data and sharing results.

In this post, we (the JupyterGIS team) introduce two new features that move JupyterGIS further in that direction. First, a STAC browser integrated directly into the JupyterGIS user interface, allowing users to explore SpatioTemporal Asset Catalogs and add selected items to a project as map layers. Second, a new Story Map feature, inspired by existing GIS storytelling tools, which makes it possible to combine maps and narrative content in a single, interactive view.

Together, these additions aim to make JupyterGIS not only a place to analyze geospatial data, but also a place to explore datasets and communicate results, all within Jupyter.

Exploring STAC Catalogs in JupyterGIS

The SpatioTemporal Asset Catalog (STAC) specification provides a common way to describe and access geospatial datasets, particularly large collections of Earth observation data. By standardizing how data and metadata are exposed, STAC enables tools to discover and query datasets across different providers using consistent spatial, temporal, and property-based criteria.

JupyterGIS now includes a STAC browser user interface that allows users to explore STAC catalogs directly from within a project. The browser makes use of the STAC Filter extension, enabling users to define rich queries on catalog items. Based on the catalog metadata, JupyterGIS automatically generates the corresponding UI components for defining these filters, making it possible to refine searches and add selected items to a project as layers without leaving the Jupyter environment.

Exploring Sentinel 3 STAC in JupyterGIS

At the moment, the STAC browser ships with a small set of preconfigured catalogs: CDSE’s Copernicus catalog, CNES’s Geodes catalog, and University of Southampton’s WorldPop. This limited selection reflects the early stage of the feature. Work is already underway by the Eric and Wendy Schmidt Center for Data Science & Environment (DSE) at UC Berkeley to allow users to connect to arbitrary STAC catalogs by providing their own catalog URL, and to introduce a catalog of STAC catalogs to make discovering and configuring additional data sources easier in future releases.

Story Maps in JupyterGIS

Communicating geospatial results often requires more than interactive maps alone. To address this, JupyterGIS now introduces a Story Map feature that makes it possible to combine narrative content with map-based views inside a single interface. Inspired by existing GIS storytelling tools, this feature allows users to structure a sequence of markdown text and map position, where each step can capture a specific state of the project, including visible layers, layers symbology, and map extent.

Story Maps in JupyterGIS are designed to complement exploratory and analytical workflows, providing a lightweight way to present results directly from the same environment in which they were produced. This makes it easier to move from data exploration to communication without exporting projects to external tools.

Try it live in your browser by clicking on the following link, https://notebook.link/@quantstack/earthquakes-story-map, hosted by notebook.link, a free service for sharing Jupyter documents.

JupyterGIS story maps showcased in notebook.link

Acknowledgments

This work on JupyterGIS by QuantStack was funded by the French National Centre for Space Studies (CNES).

About the author

Greg Mooney is a Scientific Computing Developer at QuantStack. He is one of the core developers of JupyterGIS, the author of jupyterlab-gather, and a contributor to several other Jupyter extensions.

JupyterGIS

2024-06-12T17:09:00+00:00

Pioneering Web-based, Collaborative, and Open-source GIS Tools

By Sylvain Corlay [1], Anne Fouilloux [2], and Monika Weissschnur [3]

We are thrilled to announce that the European Space Agency (ESA) is funding our proposal “Real-time collaboration and collaborative editing for GIS workflows with Jupyter and QGIS.”

The consortium spearheading this project comprises QuantStack and Simula Research Lab (Simula), two organizations with a long history of contributions to the Jupyter project and the broader open-source scientific computing ecosystem.

The goal of the project is to build solid foundations for a versatile web-based user interface for Geographic Information Systems (GIS) workflows. It will comprise several components, including a JupyterLab extension for collaboratively editing QGIS project files and the integration of these APIs in the Jupyter Notebook. We will then explore integration with platforms like the Copernicus Data Space Ecosystem (CDSE) and the European Open Science Cloud (EOSC).

Collaborative workflows in geosciences

Collaborative editing of documents has become an integral part of our digital lives and has made us collectively more productive. Gone are the days of cumbersome email exchanges with documents shuttling back and forth.

Looking ahead, the potential of co-editing extends far beyond text documents and will apply to all authoring UIs, from CAD to image processing. We think that the shift to collaborative editing will be even more transformative for larger and more complex projects, which are inherently social and require the concerted effort of large teams. Whether you are designing a stadium, a plane, or an ocean liner, you need to coordinate a diverse expertise to build a unified model. In geosciences, it may range from climate modeling to agriculture, ecology, urban planning, and many more areas of expertise. For such endeavors, we must embrace tools favoring collaboration, and this applies to the future web-based user interfaces for geoscience research.

We have been working on collaborative editing in the core of JupyterLab for the past three years. Our approach is based on the Yjs framework, an implementation of CRDT data structures (Conflict-free Replicated Data Type). We have learned that since it is so tied to the data model, retrofitting these features into an existing application is considerably more arduous than building the initial data model on the appropriate paradigm from inception. This is why JupyterGIS will be built from the ground up with collaborative editing in mind.

JupyterGIS will be the first open-source GIS tool to provide collaborative editing features.

Importantly, JupyterGIS is part of a broader mission to enable collaborative workflows in open-source technical computing. The JupyterCAD project, a collaborative CAD modeler, is another example of this endeavor. By sharing knowledge and resources, both JupyterGIS and JupyterCAD will benefit from and contribute to each other’s progress, ultimately driving innovation and productivity in their respective fields.

From the desktop to the web

Advanced authoring tools, including IDEs, CAD modelers, image processing software, and GIS applications, are essential for professionals who rely on them for extended periods. These users have high expectations and demand key features to optimize their productivity and workflow. Among these features are extensibility with plugins, configurable keyboard shortcuts, themability, internationalization, scriptability, a unified settings system, and the ability to operate across multiple browser windows and devices.

Developing a new application from the ground up that meets all these requirements is a formidable challenge. This is where the JupyterLab application framework proves invaluable. By leveraging this framework, developers can build custom, feature-rich authoring tools that incorporate these essential features from the outset, making it an ideal solution for creating modern, user-centric applications.

Beyond the pure authoring user interface, for which JupyterLab is a great tool, it is also the perfect tool for integration with notebook-based workflows. As part of this project, we will develop an advanced Python API to manage JupyterGIS sessions, leveraging Jupyter’s robust display system to incorporate sophisticated GIS features inline in Jupyter notebooks and consoles. This integration will further enhance the capabilities and versatility of our application. This feature will follow the same architecture as that of JupyterCAD for the integration with the notebook user interface.

Real-world applications

JupyterGIS is meant to become a general-purpose tool. However, we will work with teams of practitioners on specific real-world applications to ensure that it addresses their needs.

One such project concerns the use of digital and GIS solutions for emergency management. While practitioners involved in emergency management already have experience with such tools, we are convinced that web-based applications built with collaboration in mind from the start can foster improved coordination and collaboration, and therefore the effectiveness of the response.

Response teams comprise different organizations, including the affected municipalities, police, emergency response organizations like the Red Cross, and advisers such as the U.S. Army Corps of Engineers (USACE) in the United States or the Norwegian Water Resources and Energy Directorate (NVE) in Norway. Even when using the same incident management system, miscommunications and misunderstandings can occur between these stakeholders, resulting in delays or improper use of resources. An improved integration of collaborative GIS software can improve upon the existing tooling.

Building collaborations in Europe and beyond

We will partner with key practitioners and make sure JupyterGIS addresses their use cases. Integration in the Copernicus Data Space Ecosystem (CDSE) and the European Open Science Cloud (EOSC) will be key to the adoption of JupyterGIS and demonstrate its deployment in such environments.

Beyond Europe, we are currently working on including this project into a broader scope. We are excited to be partnering with the “LIVE-Env” project, an open-source initiative spearheaded by Alyssa Goodman at Harvard University. Furthermore, we are establishing a key collaboration with the Berkeley Institute for Data Sciences (BIDS) and the Schmidt Center for Data Science and Environment (DSE) at UC Berkeley and 2i2c, with a focus on Jupyter-based geosciences.

Affiliations

[1] QuantStack[2] Simula[3] Simula