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22:00 FOSS4G, Open Source Geospatial Foundation (OSGeo) English 2016

The new PyWPS-4: your Python based WPS server (PyWPS project report)

PyWPS is an open source, light-weight, Python based, implementation of the OGC Web Processing Service (WPS) standard. It provides users with a relatively seamless environment where to code geo-spatial functions and models that are readily exposed to the Internet through the WWW. Initially started in 2006, PyWPS has been completely re-written for PyWPS-4 taking advantage of the state-of-the-art Python infrastructure in order to provide new and useful features. The current version 3 implements the WPS 1.0 standard almost entirely. The recent publication of WPS version 2.0 - which brings forth important new functionalities - is also prompting this re-structuring of the code for PyWPS-4. PyWPS offers a straightforward WPS development framework with the increasingly popular Python language. Python offers easy access to a vast array of code libraries that can be easily used in the processes, in particular those for geo-spatial data manipulation, e.g. GRASS, GDAL/OGR, Fiona, Shapely, etc., but also to statistics packages (e.g. rpy2 for R statistics) and data analysis tools (e.g. pandas). PyWPS offers storage mechanisms for process inputs and outputs and spawns processes to the background for asynchronous execution requests. Future goals of the project include automatic publication of geo-spatial results through a WFS/WCS server such as MapServer and Geoserver and support for Transactional WPS with a process scheduler. The authors present general project news like to on going OSGeo incubation and the new Project Steering Committee as well as the current state of PyWPS, and show demonstrations how these services are currently being provided.
  • Published: 2016
  • Publisher: FOSS4G, Open Source Geospatial Foundation (OSGeo)
  • Language: English
30:46 FOSS4G, Open Source Geospatial Foundation (OSGeo) English 2016

Standard-compliant geoprocessing services for Earth Observation time-series data access and analysis

Earth Observation time-series data are valuable information to monitor the change of the environment. But access to data and the execution of analysis tools are often time-consuming tasks and data processing knowledge is required. In order to allow user-friendly applications to be built, tools are needed to simplify the access to data archives and the analysis of such time-series data. In this work, web services for accessing and analyzing MODIS, Landsat, and Sentinel time-series data have been developed based on the Web Processing Service specification of the Open Geospatial Consortium and made available within the Earth Observation Monitor framework. The Python library "pyEOM" has been developed to combine access and analysis tools for Earth Observation time-series data. Algorithms developed to analyze vegetation changes are provided as web-based processing services in connection to the prior developed access services as well. Using the services developed, users only need to provide the geometry and the name of the dataset the user is interested in; any processing is done by the web service. The services and applications (web and mobile) are based on geospatial open source software.
  • Published: 2016
  • Publisher: FOSS4G, Open Source Geospatial Foundation (OSGeo)
  • Language: English
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