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Choang Club

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@choangclubs • Joined Jan 2025 • Active 12 hours ago

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First Name	Choang
Last Name	Club
Nickname	choangclubs
Short Description	ChoangClub là cổng game bài đổi thưởng quốc tế uy tín Hiện tại, ChoangClub đang được yêu thích ở Việt Nam Tải Choáng ngay cho iOS, Android, APK trải nghiệm Website: https://siliconharbormag.com/ SĐT: 0381472566 Email: siliconharbormag@gmail.com Địa chỉ: 278 Hoàng Hoa Thám, Phường 12, Tân Bình, Hồ Chí Minh, Việt Nam Hashtags: #choangclub #trangchuchoangclub #linkchoangclubmoinhat #siliconharbormagcom LINKPROFILE https://siliconharbormag.com/ https://x.com/choangclubs https://www.pinterest.com/choangclubs/ https://www.youtube.com/@siliconharbormag https://www.twitch.tv/choangclubs https://www.tumblr.com/choangclubs https://500px.com/p/choangclubs?view=photos https://www.reddit.com/user/Bitter-Emphasis-6668/ http://emseyi.com/user/choangclubs https://www.behance.net/choangclub10 https://gravatar.com/exactlyca33e488ee https://www.instapaper.com/p/choangclubs https://www.bitchute.com/channel/7zkL8vW7XErv https://qiita.com/choangclubs 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More

References and Further Reading

1.1 What is a Platform

Further reading

Earth Observation Cloud Platform Concept Development Study Report: https://docs.ogc.org/per/21-023.html
Big Earth data: disruptive changes in Earth observation data management and analysis? https://www.tandfonline.com/doi/full/10.1080/17538947.2019.1585976
Enabling the Big Earth Observation Data via Cloud Computing and DGGS: Opportunities and Challenges: https://www.mdpi.com/2072-4292/12/1/62
An Overview of Platforms for Big Earth Observation Data Management and Analysis: https://www.mdpi.com/2072-4292/12/8/1253
The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities: https://www.mdpi.com/2072-4292/13/6/1125
ESA Earth Observation Exploitation Platform Common Architecture (EOEPCA): https://eoepca.org

1.2 What is a Data Cube?

Further Reading

David Montero Loaiza et al (2023). Data Cubes for Earth System Research: Challenges Ahead. Earth ArXiv (Preprint). https://doi.org/10.31223/X58M2V
Popular Data Cube Management Systems:

References

openeo.org (2022). What are Data Cubes.
ESA (2018). Earth System Data Lab.
Maximilian Söchting (2022). LexCube – Leipzig Explorer of Earth Data Cubes

1.3 Open Science, Open Data and the FAIR Principles

Further Reading

Help for understanding licenses and choosing the right Open Source license

Legal (FOSSA) (2023). Software Licencses In Plain English.
GitHub Inc. (2023) Choose an open source license.

And plentiful resources on open source projects, how to contribute and incorporate them into your work

opensource.com (2023). Open Source resources.

References

European Commission (2022). What is open data?. https://data.europa.eu/elearning/en/module1/#/id/co-01 –
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Open Knowledge Foundation (2022). Open Data Handbook.
Fürst, Elena, Gänsdorfer, Nikos, Kalová, Tereza, Macher, Therese, Schranzhofer, Hermann, Stork, Christiane, & Thöricht, Heike (2022). Open Educational Resources Research Data Management. DOI: 10.5281/zenodo.6923397
ARDC Ltd. (2022). How to Make Your Data FAIR. DOI: 10.5281/zenodo.7426145.
GO FAIR (2022). The FAIRification Process.
Mapscaping Podcast with Paul Ramsey (2021). GDAL – Geospatial Data Abstraction Library
CEOS (2022). CEOS Analysis Ready Data.

2.1 Data Discovery

Further Reading

STAC tutorials
Echoes in space course

References

STAC
OWSLib
STAC Python Client (pystac_client)
radiantearth.github.io/stac-browser
OpenSearch specification

2.2 Data Properties

Further Reading

Standartized data management with STAC
STAC sessions from FOSS4G 2023

2.3 Data Access

References

openeo.org – About OpenEO
Edzer Pebesma, Wolfgang Wagner, Jan Verbesselt, Erwin Goor, Christian Briese, Markus Neteler (2016). OpenEO: a GDAL for Earth Observation Analytics. https://r-spatial.org/2016/11/29/openeo.html
openeo.org – Project Deliverable 22
openeo.org (2022). Processes on Datacubes

3.2 Result Validation

Further Reading

Video: Meyer, Hanna: Machine learning-based maps of the environment: challenges of extrapolation and overfitting. OpenGeoHub Summer School 2022 – KISTE project workshop, OpenGeoHub Foundation, 2022. https://doi.org/10.5446/59412

References

Meyer, H., Pebesma, E. Machine learning-based global maps of ecological variables and the challenge of assessing them. Nat Commun 13, 2208 (2022). https://doi.org/10.1038/s41467-022-29838-9
Meyer, H., & Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620–1633. https://doi.org/10.1111/2041-210X.13650
Loew et al. (2017): https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2017RG000562
Brenning et al. (2012): https://doi.org/10.1109/IGARSS.2012.6352393
Valavi et al. (2019): https://doi.org/10.1111/2041-210X.13107
Roberts et al. (2017): https://doi.org/10.1111/ecog.02881
Ploton et al. (2020): https://doi.org/10.1038/s41467-020-18321-y
Milà et al. (2022): https://doi.org/https://doi.org/10.1111/2041-210X.13851
Linnenbrink et al. (2023): https://doi.org/10.5194/egusphere-2023-1308
Sabatini et al. (2022): https://doi.org/10.1038/s41467-022-32063-z