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| PyWPS | |
|---|---|
| Name | PyWPS |
| Developer | Python Software Foundation, MapServer Project contributors |
| Released | 2006 |
| Programming language | Python |
| Operating system | Cross-platform |
| License | MIT License |
PyWPS PyWPS is an open-source implementation of the Open Geospatial Consortium Web Processing Service standard written in Python (programming language). It enables the publishing, discovery, and execution of geospatial processes on servers such as GeoServer, MapServer, and cloud platforms including Amazon Web Services and Microsoft Azure. Developers and organizations use it alongside projects like GDAL, PostGIS, QGIS, Esri, and OpenStreetMap to expose raster, vector, and analytic operations in interoperable ways.
PyWPS implements the Open Geospatial Consortium's Web Processing Service interface to offer HTTP-based process orchestration, synchronous and asynchronous execution, and results delivery. It integrates with geospatial libraries like GDAL and PROJ and databases like PostgreSQL with PostGIS extension to operate on spatial datasets such as those from Copernicus Programme missions, Landsat (satellite), and Sentinel (satellite) constellations. Commonly paired with GeoServer for Web Map Service capabilities and with MapProxy for caching, PyWPS supports standards used by European Space Agency, National Aeronautics and Space Administration, and national mapping agencies.
PyWPS originated in the mid-2000s in response to the increasing adoption of OGC service standards driven by projects like INSPIRE and initiatives from Open Geospatial Consortium. Early development overlapped with work on MapServer and GDAL/OGR wrappers, influenced by contributors affiliated with University of Twente, Stockholm University, and European research programmes such as FP6 and FP7. Over successive releases PyWPS added features to align with OGC WPS versions and to support integration with container technologies such as Docker as cloud computing and orchestration platforms like Kubernetes emerged.
PyWPS follows a modular architecture with components for service endpoints, process definitions, and execution backends. Core modules map WPS operations—GetCapabilities, DescribeProcess, Execute—to HTTP endpoints interoperable with clients like OpenLayers, Leaflet (JavaScript library), and pygeoapi. Process implementations call libraries such as NumPy, SciPy, and Rasterio for computation, while data I/O leverages GDAL and fiona (software). Execution backends can dispatch jobs synchronously, asynchronously via Celery (software), or through system schedulers used by European Grid Infrastructure and HPC centers like CERN clusters. Integration points include authentication providers such as Keycloak, identity federations like eduGAIN, and storage services from Google Cloud Platform and Amazon S3.
PyWPS supports OGC-conformant capabilities: machine-readable service metadata, process discovery, parameterized execution, and result encoding in formats like GeoJSON, GML, NetCDF, and GeoTIFF. It handles complex inputs including WPS-compliant bounding boxes referencing CRS definitions from EPSG registry and coordinate transforms via PROJ (library). Clients may request asynchronous execution with status monitoring, leveraging message brokers such as RabbitMQ and databases like SQLite or PostgreSQL for state persistence. Advanced features include streaming outputs, process chaining compatible with Business Process Model and Notation, and integration with catalog services like CSW and CKAN for dataset discovery.
Deployments of PyWPS occur on web servers such as Apache HTTP Server with mod_wsgi or on application servers using Gunicorn and uWSGI. Containerized deployments rely on Docker Hub images orchestrated via Kubernetes or Docker Compose for scalable workflows. Integration examples include pairing with GeoServer for WMS/WFS, front ends built on OpenLayers or Leaflet (JavaScript library), and workflow engines like Apache Airflow for scheduled processing. Enterprises often combine PyWPS with identity and access systems such as OAuth 2.0 providers and logging solutions like ELK Stack for observability.
PyWPS powers a variety of geospatial services: near-real-time processing of satellite telemetry for agencies like European Centre for Medium-Range Weather Forecasts, hydrological modelling for research groups at UNESCO partner institutes, and cadastral analytics used by municipal authorities such as City of Helsinki pilots. Scientific workflows include climate downscaling referencing Intergovernmental Panel on Climate Change scenarios, land cover classification using machine learning libraries like TensorFlow and scikit-learn, and hazard mapping for crisis response coordinated with organizations like United Nations Office for the Coordination of Humanitarian Affairs.
Development of PyWPS involves contributors from academic institutions, national mapping agencies, and private companies, collaborating through channels including GitHub, mailing lists hosted by organizations like OSGeo, and events such as FOSS4G and State of the Map. Governance often mirrors community-led open-source projects with maintainers reviewing contributions and continuous integration via services like Travis CI and GitLab CI/CD. Documentation and example deployments are shared at conferences including AGU Fall Meeting and workshops hosted by European Space Agency working groups.
Category:Geospatial software