scholarly journals PODPAC: open-source Python software for enabling harmonized, plug-and-play processing of disparate earth observation data sets and seamless transition onto the serverless cloud by earth scientists

2020 ◽  
Vol 13 (4) ◽  
pp. 1507-1521
Author(s):  
Mattheus P. Ueckermann ◽  
Jerry Bieszczad ◽  
Dara Entekhabi ◽  
Marc L. Shapiro ◽  
David R. Callendar ◽  
...  
Keyword(s):  
Open Source ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Plug And Play ◽  
Seamless Transition ◽  
Earth Observation Data

Mapping slums and model population density using earth observation data and open source solutions

2019 ◽  
Author(s):  
Tais Grippa ◽  
Stefanos Georganos ◽  
Sabine Vanhuysse ◽  
Moritz Lennert ◽  
Nicholus Mboga ◽  
...  
Keyword(s):  
Population Density ◽  
Open Source ◽  
Earth Observation ◽  
Observation Data ◽  
Model Population ◽  
Earth Observation Data

scholarly journals Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery

2019 ◽  
Vol 11 (8) ◽  
pp. 907 ◽  
Author(s):  
Vasileios Syrris ◽  
Paul Hasenohr ◽  
Blagoj Delipetrev ◽  
Alexander Kotsev ◽  
Pieter Kempeneers ◽  
...  
Keyword(s):  
Random Forests ◽  
Computational Models ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Computational Aspects ◽  
Learning Set ◽  
Earth Observation Data ◽  
Class Segmentation ◽  
Sentinel 2

Motivated by the increasing availability of open and free Earth observation data through the Copernicus Sentinel missions, this study investigates the capacity of advanced computational models to automatically generate thematic layers, which in turn contribute to and facilitate the creation of land cover products. In concrete terms, we assess the practical and computational aspects of multi-class Sentinel-2 image segmentation based on a convolutional neural network and random forest approaches. The annotated learning set derives from data that is made available as result of the implementation of European Union’s INSPIRE Directive. Since this network of data sets remains incomplete in regard to some geographic areas, another objective of this work was to provide consistent and reproducible ways for machine-driven mapping of these gaps and a potential update of the existing ones. Finally, the performance analysis identifies the most important hyper-parameters, and provides hints on the models’ deployment and their transferability.

Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India

2017 ◽  
Vol 33 (11) ◽  
pp. 1202-1222 ◽  
Author(s):  
Sudhir Kumar Singh ◽  
Prosper Basommi Laari ◽  
Sk. Mustak ◽  
Prashant K. Srivastava ◽  
Szilárd Szabó
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Earth Observation Data ◽  
Tons River Basin

scholarly journals Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets

2016 ◽  
pp. 1-15 ◽  
Author(s):  
Sudhir Kumar Singh ◽  
Prashant K. Srivastava ◽  
Szilárd Szabó ◽  
George P. Petropoulos ◽  
Manika Gupta ◽  
...  
Keyword(s):  
Land Cover ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Spatial Metrics ◽  
Land Cover Dynamics ◽  
Earth Observation Data

scholarly journals Open Source Framework for Enabling HPC and Cloud Geoprocessing Services

2020 ◽  
Vol 12 (4) ◽  
pp. 61-76
Author(s):  
José Miguel Montañana ◽  
Paolo Marangino ◽  
Antonio Hervás
Keyword(s):  
Big Data ◽  
Open Source ◽  
Precision Agriculture ◽  
Data Analytics ◽  
Performance Metrics ◽  
Earth Observation ◽  
Observation Data ◽  
Open Source Framework ◽  
Earth Observation Data ◽  
Geoprocessing Services

Geoprocessing is a set of tools that can be used to efficiently address several pressing chal-lenges for the global economy ranging from agricultural productivity, the design of transport networks, to the prediction of climate change and natural disasters. This paper describes an Open Source Framework developed, within three European projects, for Ena-bling High-Performance Computing (HPC) and Cloud geoprocessing services applied to agricultural challenges. The main goals of the European Union projects EUXDAT (EUro-pean e-infrastructure for eXtreme Data Analytics in sustainable developmenT), CYBELE (fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics), and EOPEN (opEn interOperable Platform for unified access and analysis of Earth observatioN data) are to enable the use of large HPC systems, as well as big data management, user-friendly access and visualization of results. In addition, these projects focus on the development of software frameworks, and fuse Earth-observation data, such as Copernicus data, with non-Earth-observation data, such as weather, environmental and social media information. In this paper, we describe the agroclimatic-zones pilot used to validate the framework. Finally, performance metrics collected during the execution (up to 182 times speedup with 256 MPI processes) of the pilot are presented.

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