scholarly journals JupyTEP IDE as an Online Tool for Earth Observation Data Processing

2019 ◽  
Vol 11 (17) ◽  
pp. 1973 ◽  
Author(s):  
Rapiński ◽  
Bednarczyk ◽  
Zinkiewicz
Keyword(s):  
User Interface ◽  
Data Processing ◽  
European Space Agency ◽  
Earth Observation ◽  
Observation Data ◽  
Cloud Infrastructure ◽  
Integrated Development Environment ◽  
Development Environment ◽  
Integrated Development ◽  
Earth Observation Data

The paper describes a new tool called JupyTEP integrated development environment (IDE), which is an online integrated development environment for earth observation data processing available in the cloud. This work is a result of the project entitled “JupyTEP IDE—Jupyter-based IDE as an interactive and collaborative environment for the development of notebook style EO algorithms on network of exploitation platforms infrastructure” carried out in cooperation with European Space Agency. The main goal of this project was to provide a universal earth observation data processing tool to the community. JupyTEP IDE is an extension of Jupyter software ecosystem with customization of existing components for the needs of earth observation scientists and other professional and non-professional users. The approach is based on configuration, customization, adaptation, and extension of Jupyter, Jupyter Hub, and Docker components on earth observation data cloud infrastructure in the most flexible way; integration with accessible libraries and earth observation data tools (sentinel application platform (SNAP), geospatial data abstraction library (GDAL), etc.); adaptation of existing web processing service (WPS)-oriented earth observation services. The user-oriented product is based on a web-related user interface in the form of extended and modified Jupyter user interface (frontend) with customized layout, earth observation data processing extension, and a set of predefined notebooks, widgets, and tools. The final IDE is addressed to the remote sensing experts and other users who intend to develop Jupyter notebooks with the reuse of embedded tools, common WPS interfaces, and existing notebooks. The paper describes the background of the system, its architecture, and possible use cases.

Advanced Feature Extraction for Earth Observation Data Processing

2018 ◽  
pp. 108-133
Author(s):  
E. Izquierdo-Verdiguier ◽  
V. Laparra ◽  
J Muñoz-Marí ◽  
L. Gómez-Chova ◽  
G. Camps-Valls
Keyword(s):  
Feature Extraction ◽  
Data Processing ◽  
Earth Observation ◽  
Observation Data ◽  
Earth Observation Data

scholarly journals Multi-Mission Earth Observation Data Processing System

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3831 ◽  
Author(s):  
Mhangara ◽  
Mapurisa
Keyword(s):  
Image Processing ◽  
Data Processing ◽  
Atmospheric Correction ◽  
Processing System ◽  
Value Added ◽  
Earth Observation ◽  
Open Architecture ◽  
Observation Data ◽  
Data Processing System ◽  
Earth Observation Data

The surge in the number of earth observation satellites being launched worldwide is placing significant pressure on the satellite-direct ground receiving stations that are responsible for systematic data acquisition, processing, archiving, and dissemination of earth observation data. Growth in the number of satellite sensors has a bearing on the ground segment payload data processing systems due to the complexity, volume, and variety of the data emanating from the different sensors. In this paper, we have aimed to present a generic, multi-mission, modularized payload data processing system that we are implementing to optimize satellite data processing from historical and current sensors, directly received at the South African National Space Agency’s (SANSA) ground receiving station. We have presented the architectural framework for the multi-mission processing system, which is comprised of five processing modules, i.e., the data ingestion module, a radiometric and geometric processing module, atmospheric correction and Analysis Ready Data (ARD) module, Value Added Products (VAPS) module, and lastly, a packaging and delivery module. Our results indicate that the open architecture, multi-mission processing system, when implemented, eliminated the bottlenecks linked with proprietary mono-mission systems. The customizable architecture enabled us to optimize our processing in line with our hardware capacities, and that resulted in significant gains in large-scale image processing efficiencies. The modularized, multi-mission data processing enabled seamless end-to-end image processing, as demonstrated by the capability of the multi-mission system to execute geometric and radiometric corrections to the extent of making it analysis-ready. The processing workflows were highly scalable and enabled us to generate higher-level thematic information products from the ingestion of raw data.

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