| Geo Engine is a platform for analyzing and visualizing biodiversity data that contains geographic information such as rasters, points, and polygons. The frontend serves as a web-based geographic information system and can be customized as a community-specific application. It originated from the VAT system of GFBio and has been developed by Geo Engine GmbH as an open-source product. |
Overview
Geo Engine is a platform for processing and analyzing biodiversity data with geographic information. It consists of several components. The backend manages access to the data and its processing and supports various data standards for raster data and vector data via the popular GDAL translator library. The backend provides the functionality via a web service layer (APIs). The frontend serves either as a Geographic Information System (GIS) with full functionality or as a component library for building custom portals and dashboards. For example, The Geo Engine Python Library allows users to interact programmatically with a Geo Engine backend. The GIS-based option is used for the implementation of the VAT system which is a community-agnostic service in the Application Layer. The component library can be used for creating community-specific applications, e.g. a custom dashboard for exploring and working with a specific data product offered in the Semantic Layer.
Status: Weblink: Target group: Users and developers of data products Keywords: Visualization, analysis, transformation, geospatial RDC Integration: Connected Product owner: Uni Marburg (UMR) |
RDC Integration
Geo Engine offers good integration into the RDC. The VAT instance is running fully containerized on the de.NBI cloud and by supporting SSO (Single Sign-On) that allows users to login via their GFBio credentials. A prototypical implementation exists for accessing data from the Aruna Object Storage. Furthermore, the VAT system is connected to the Query Portal (GFBio Search) through the GFBio Data Collection Service.
Getting started
The easiest way to become familiar with Geo Engine is to take a look at the publicly accessible VAT instance, which is a community-agnostic GIS web application backed by Geo Engine with several biodiversity research related datasets available.
You can also use the Python package, either connecting to the VAT instance's backend, which runs at https://vat.gfbio.org/api/, or to your own backend. For example, the package can be used in Jupyter Notebooks to construct and retrieve data products from a Geo Engine backend, taking advantage of Geo Engine's powerful geospatial processing capabilities. For a data product loaded into Python, any suitable visualization tool can be used within the notebook. Furthermore, when connected to the same Geo Engine backend, a user can seemlessly switch between the Geo Engine web front end (VAT) and the Geo Engine Python Library, choosing the tool best suited for the task at hand any time.
For running your own backend (and frontend) take a look at the backend and frontend GitHub repositories. You can find instructions on how to compile and run them in the respective readme files.
User Guide
Geo Engine provides many operators which can be applied to spatio-temporal datasets. Several datasets and operator invocations can be combined in workflows to create arbitrarily complex processing pipelines whose results can be further reused through the Python Library and visualized in the frontend.
There is documentation available where you can learn more about Geo Engine's architecture and e.g. all operators available. Also see the Python API reference and the Jupyter Notebook examples.
Developer Guide
Since Geo Engine is open-source, you can easily extend and customize it to your own needs. Most relevant for creating new applications are the options to add datasets, data providers and create custom dashboards.
As an admin of an existing Geo Engine instance you can already manage datasets via the Python API (https://python.docs.geoengine.io/datasets.html), but you also have the option to extend the backend with entirely new data provider implementations here.
References
Beilschmidt, C., Drönner, J., Mattig, M., & Seeger, B. (2023). Geo Engine: Workflow-driven Geospatial Portals for Data Science. Datenbank-Spektrum, 1-9.
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