Continued from B5: Adaptive, Distributed and Scalable Analysis of Massive Satellite Data
Description
DAWs for satellite data analysis are highly heterogeneous and complex in terms of input data, software, and resource requirements. Furthermore, data for DAWs are often available at different data centers, with data download being a major bottleneck in DAWs execution.
Our goal is to expand orchestration of Earth Observation Workflows (EOWs) from FONDA I to a federated multi-center scenario to enable analysis of changes in agricultural land use over very large, heterogeneous and distributed data sets.
Scientists
- Felix Kummer
- Katarzyna Ewa Lewińska
Publications
29 entries « ‹ 3 of 3
› » 2022
Hilbrich, Marcus; Lehmann, Fabian
ßMACH — A Software Management Guidance Proceedings Article
In: Reichelt, David Georg; Müller, Richard; Becker, Steffen; Hasselbring, Wilhelm; Hoorn, André; Kounev, Samuel; Koziolek, Anne; Reussner, Ralf (Ed.): Symposium on Software Performance 2021, CEUR-WS, Leipzig, Germany, 2022.
@inproceedings{ssp21:ssmach,
title = {ßMACH — A Software Management Guidance},
author = {Marcus Hilbrich and Fabian Lehmann},
editor = {David Georg Reichelt and Richard Müller and Steffen Becker and Wilhelm Hasselbring and André Hoorn and Samuel Kounev and Anne Koziolek and Ralf Reussner},
url = {http://ceur-ws.org/Vol-3043/},
year = {2022},
date = {2022-02-01},
urldate = {2022-02-01},
booktitle = {Symposium on Software Performance 2021},
publisher = {CEUR-WS},
address = {Leipzig, Germany},
abstract = {Creating, maintaining, and operating software artifacts is a long ongoing challenge. Various management strategies have been developed and are frequently used. Nevertheless, a unification of describing the management strategies to compare them is an open question. We present ßMACH as an answer. ßMACH allows systematic descriptions and checks independently from the management strategy. In this paper, we test parts of ßMACH on the example of performance requirements. So we applied ßMACH
to V-Model and Scrum.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Creating, maintaining, and operating software artifacts is a long ongoing challenge. Various management strategies have been developed and are frequently used. Nevertheless, a unification of describing the management strategies to compare them is an open question. We present ßMACH as an answer. ßMACH allows systematic descriptions and checks independently from the management strategy. In this paper, we test parts of ßMACH on the example of performance requirements. So we applied ßMACH
to V-Model and Scrum.
Frantz, David; Hostert, Patrick; Rufin, Philippe; Ernst, Stefan; Röder, Achim; Linden, Sebastian
Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics Journal Article
In: Remote Sensing, vol. 14, no. 3, 2022, ISSN: 2072-4292.
@article{rs14030597,
title = {Revisiting the Past: Replicability of a Historic Long-Term Vegetation Dynamics Assessment in the Era of Big Data Analytics},
author = {David Frantz and Patrick Hostert and Philippe Rufin and Stefan Ernst and Achim Röder and Sebastian Linden},
url = {https://www.mdpi.com/2072-4292/14/3/597},
doi = {10.3390/rs14030597},
issn = {2072-4292},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Remote Sensing},
volume = {14},
number = {3},
abstract = {Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth’s land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based on static image selection strategies should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. We recommend using all available data and taking advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Open and analysis-ready data, as well as methodological and technical advancements have resulted in an unprecedented capability for observing the Earth’s land surfaces. Over 10 years ago, Landsat time series analyses were inevitably limited to a few expensive images from carefully selected acquisition dates. Yet, such a static selection may have introduced uncertainties when spatial or inter-annual variability in seasonal vegetation growth were large. As seminal pre-open-data-era papers are still heavily cited, variations of their workflows are still widely used, too. Thus, here we quantitatively assessed the level of agreement between an approach using carefully selected images and a state-of-the-art analysis that uses all available images. We reproduced a representative case study from the year 2003 that for the first time used annual Landsat time series to assess long-term vegetation dynamics in a semi-arid Mediterranean ecosystem in Crete, Greece. We replicated this assessment using all available data paired with a time series method based on land surface phenology metrics. Results differed fundamentally because the volatile timing of statically selected images relative to the phenological cycle introduced systematic uncertainty. We further applied lessons learned to arrive at a more nuanced and information-enriched vegetation dynamics description by decomposing vegetation cover into woody and herbaceous components, followed by a syndrome-based classification of change and trend parameters. This allowed for a more reliable interpretation of vegetation changes and even permitted us to disentangle certain land-use change processes with opposite trajectories in the vegetation components that were not observable when solely analyzing total vegetation cover. The long-term budget of net cover change revealed that vegetation cover of both components has increased at large and that this process was mainly driven by gradual processes. We conclude that study designs based on static image selection strategies should be critically evaluated in the light of current data availability, analytical capabilities, and with regards to the ecosystem under investigation. We recommend using all available data and taking advantage of phenology-based approaches that remove the selection bias and hence reduce uncertainties in results.
Hilbrich, Marcus; Lehmann, Fabian
Discussing Microservices: Definitions, Pitfalls, and their Relations Proceedings Article
In: 2022 IEEE International Conference on Services Computing (SCC), pp. 39-44, 2022.
@inproceedings{9860130,
title = {Discussing Microservices: Definitions, Pitfalls, and their Relations},
author = {Marcus Hilbrich and Fabian Lehmann},
doi = {10.1109/SCC55611.2022.00019},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 IEEE International Conference on Services Computing (SCC)},
pages = {39-44},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick
Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany Journal Article
In: Remote sensing of environment, vol. 269, pp. 112831, 2022.
@article{blickensdorfer2022mapping,
title = {Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany},
author = {Lukas Blickensdörfer and Marcel Schwieder and Dirk Pflugmacher and Claas Nendel and Stefan Erasmi and Patrick Hostert},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Remote sensing of environment},
volume = {269},
pages = {112831},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Schug, Franz; Frantz, David; Okujeni, Akpona; Hostert, Patrick
Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and-2 data Journal Article
In: Remote Sensing Letters, vol. 13, no. 8, pp. 822–832, 2022.
@article{schug2022sub,
title = {Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and-2 data},
author = {Franz Schug and David Frantz and Akpona Okujeni and Patrick Hostert},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Remote Sensing Letters},
volume = {13},
number = {8},
pages = {822–832},
publisher = {Taylor & Francis},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Katerndahl, Florian; Pflugmacher, Dirk; Lehmann, Fabian; Janz, Andreas; Leser, Ulf; Hostert, Patrick
Geoflow - Novel Workflow Implementations To Facilitate Big EO Data Workflows in Nextflow Miscellaneous
2022.
@misc{Katerndahl2022,
title = {Geoflow - Novel Workflow Implementations To Facilitate Big EO Data Workflows in Nextflow},
author = {Florian Katerndahl and Dirk Pflugmacher and Fabian Lehmann and Andreas Janz and Ulf Leser and Patrick Hostert},
doi = {10.13140/RG.2.2.25203.60963},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
2021
Lehmann, Fabian; Frantz, David; Becker, Soeren; Leser, Ulf; Hostert, Patrick
FORCE on Nextflow: Scalable Analysis of Earth Observation Data on Commodity Clusters Proceedings Article
In: CIKM Workshops, 2021.
@inproceedings{Lehmann2021FORCEON,
title = {FORCE on Nextflow: Scalable Analysis of Earth Observation Data on Commodity Clusters},
author = {Fabian Lehmann and David Frantz and Soeren Becker and Ulf Leser and Patrick Hostert},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {CIKM Workshops},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Frantz, David; Schug, Franz; Okujeni, Akpona; Navacchi, Claudio; Wagner, Wolfgang; Linden, Sebastian; Hostert, Patrick
National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series Journal Article
In: Remote Sensing of Environment, vol. 252, pp. 112128, 2021.
@article{frantz2021national,
title = {National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series},
author = {David Frantz and Franz Schug and Akpona Okujeni and Claudio Navacchi and Wolfgang Wagner and Sebastian Linden and Patrick Hostert},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Remote Sensing of Environment},
volume = {252},
pages = {112128},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Haberl, Helmut; Wiedenhofer, Dominik; Schug, Franz; Frantz, David; Virág, Doris; Plutzar, Christoph; Gruhler, Karin; Lederer, Jakob; Schiller, Georg; Fishman, Tomer; Lanau, Maud; Gattringer, Andreas; Kemper, Thomas; Liu, Gang; Tanikawa, Hiroki; Linden, Sebastian; Hostert, Patrick
High-Resolution Maps of Material Stocks in Buildings and Infrastructures in Austria and Germany Journal Article
In: Environmental Science & Technology, vol. 55, no. 5, pp. 3368-3379, 2021, (PMID: 33600720).
@article{doi:10.1021/acs.est.0c05642,
title = {High-Resolution Maps of Material Stocks in Buildings and Infrastructures in Austria and Germany},
author = {Helmut Haberl and Dominik Wiedenhofer and Franz Schug and David Frantz and Doris Virág and Christoph Plutzar and Karin Gruhler and Jakob Lederer and Georg Schiller and Tomer Fishman and Maud Lanau and Andreas Gattringer and Thomas Kemper and Gang Liu and Hiroki Tanikawa and Sebastian Linden and Patrick Hostert},
url = {https://doi.org/10.1021/acs.est.0c05642},
doi = {10.1021/acs.est.0c05642},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Environmental Science & Technology},
volume = {55},
number = {5},
pages = {3368-3379},
note = {PMID: 33600720},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
29 entries « ‹ 3 of 3
› »
