Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book - snapshot
has assertion: assertion
has provenance: provenance
has publication info: pubinfo
type: Nanopublication
created: 2025-11-11T16:12:35.746+01:00
type: RoCrateNanopub

Oceanography
type: DefinedTerm

Environmental research
type: DefinedTerm

Earth observation
type: DefinedTerm

Book industry
type: IPTC
path: Economy, business and finance/Economic sector/Media/Book industry

Environmental Data Science
type: Lemma
norm score: 16.02002503128911
score: 12.8

notebook
type: Concept
norm score: 12.621359223300972
score: 9.1

imagery
type: Lemma
norm score: 13.642052565707132
score: 10.9

book
type: Concept
norm score: 14.147018030513175
score: 10.2

Environmental Data Science book
type: Phrase
norm score: 15.259409969481181
score: 15.0

Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book.
type: Sentence
norm score: 43.54354354354354
score: 43.5

POLYGON ((26.521543885417145 39.03722381230471, 26.52744474524991 39.03722381230471, 26.52744474524991 39.04105711064335, 26.521543885417145 39.04105711064335, 26.521543885417145 39.03722381230471))
polygon: 26.521543885417145 39.03722381230471, 26.52744474524991 39.03722381230471, 26.52744474524991 39.04105711064335, 26.521543885417145 39.04105711064335, 26.521543885417145 39.03722381230471
type: GeoShape

POLYGON ((119.12620106576212 39.27393119797617, 119.19563809273966 39.27393119797617, 119.19563809273966 39.30980175207518, 119.12620106576212 39.30980175207518, 119.12620106576212 39.27393119797617))
polygon: 119.12620106576212 39.27393119797617, 119.19563809273966 39.27393119797617, 119.19563809273966 39.30980175207518, 119.12620106576212 39.30980175207518, 119.12620106576212 39.27393119797617
type: GeoShape

POLYGON ((26.521543885417145 39.03722381230471, 26.52744474524991 39.03722381230471, 26.52744474524991 39.04105711064335, 26.521543885417145 39.04105711064335, 26.521543885417145 39.03722381230471))
type: Place

https://w3id.org/ro-id/42a5f00d-7eee-4dbe-85d6-c192fa6e135e/#8815d24d-9098-4f1f-a816-b01aa1349a04
name: POLYGON ((-43.2315509040757 -22.80675019122663, -43.02418396071632 -22.80675019122663, -43.02418396071632 -22.678831998280632, -43.2315509040757 -22.678831998280632, -43.2315509040757 -22.80675019122663))
polygon: -43.2315509040757 -22.80675019122663, -43.02418396071632 -22.80675019122663, -43.02418396071632 -22.678831998280632, -43.2315509040757 -22.678831998280632, -43.2315509040757 -22.80675019122663
type: GeoShape

POLYGON ((-86.82548387 20.977342054, -86.751891297 20.977342054, -86.751891297 21.033273193, -86.82548387 21.033273193, -86.82548387 20.977342054))
type: Place

service-account-enrichment
type: Agent

POLYGON ((-86.82548387 20.977342054, -86.751891297 20.977342054, -86.751891297 21.033273193, -86.82548387 21.033273193, -86.82548387 20.977342054))
polygon: -86.82548387 20.977342054, -86.751891297 20.977342054, -86.751891297 21.033273193, -86.82548387 21.033273193, -86.82548387 20.977342054
type: GeoShape

POLYGON ((119.12620106576212 39.27393119797617, 119.19563809273966 39.27393119797617, 119.19563809273966 39.30980175207518, 119.12620106576212 39.30980175207518, 119.12620106576212 39.27393119797617))
type: Place

https://w3id.org/ro-id/42a5f00d-7eee-4dbe-85d6-c192fa6e135e/#f52396ac-2798-42d4-a6f2-1bc74ae669e3
name: POLYGON ((-43.2315509040757 -22.80675019122663, -43.02418396071632 -22.80675019122663, -43.02418396071632 -22.678831998280632, -43.2315509040757 -22.678831998280632, -43.2315509040757 -22.80675019122663))
type: Place

Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book - snapshot
doi: https://doi.org/10.24424/g1bk-dv49
is finalized: False
is snapshot of: https://w3id.org/ro-id/b34facfa-cea8-48f5-89f6-f11ce00812a9
snapshoted at time: 2023-03-20 16:26:43.973361+00:00
about: Oceanography
about: Environmental research
about: Earth observation
content size: 1818485
date created: 2022-01-28 16:07:18.008253+00:00
date modified: 2024-03-05 12:17:35.087630+00:00
date published: 2022-01-28 16:07:18.008253+00:00
description: The research object refers to the Detecting floating objects using deep learning and Sentinel-2 imagery notebook published in the Environmental Data Science book.
encoding format: application/ld+json
keywords: Environmental Science
license: no-permission
main entity: Jupyter Notebook
name: Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book
creation mode: MANUAL
has geometry: https://w3id.org/ro-id/42a5f00d-7eee-4dbe-85d6-c192fa6e135e/0edc91a9-9049-4357-bad7-677880c8fd8a
has geometry: https://w3id.org/ro-id/42a5f00d-7eee-4dbe-85d6-c192fa6e135e/19f9ef3f-8678-48f5-a9ac-cf364939dcda
has geometry: https://w3id.org/ro-id/42a5f00d-7eee-4dbe-85d6-c192fa6e135e/4b55fad1-092b-4657-a004-aafc05499e18
has geometry: https://w3id.org/ro-id/42a5f00d-7eee-4dbe-85d6-c192fa6e135e/e2eba045-3c2c-44a1-aa39-06cc232d05f9
type: ResearchObject
type: SnapshotRO
type: Dataset
type: earth-scienceExecutableResearchObject
type: ExecutableResearchObject
domain: publishing
concept: notebook
concept: book
concept: detection
concept: imagery
concept: physical object
concept: learning
concept: research
concept: object
field of research: earth sciences
field of research: geology
IPTC: Book industry
IPTC: Language
IPTC: Literature
IPTC: Education
lemma: Environmental Data Science
lemma: imagery
lemma: Sentinel-2
lemma: research
lemma: notebook
lemma: object
lemma: detecting
NASA: communications and radar
NASA: engineering
phrase: Environmental Data Science book
phrase: deep learning
phrase: research object
phrase: refer to the detecting
phrase: imagery notebook
sentence: Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book.
sentence: The research object refers to the Detecting floating objects using deep learning and Sentinel-2 imagery notebook published in the Environmental Data Science book.
cite-as: Raquel Carmo, Jamila Mifdal, and Alejandro Coca-Castro. "Detecting floating objects using deep learning and Sentinel-2 imagery (Jupyter Notebook) published in the Environmental Data Science book." ROHub. Jan 28 ,2022. https://doi.org/10.24424/g1bk-dv49.

biblio
type: Folder
type: Dataset

tool
type: Folder
type: Dataset

input
type: Folder
type: Dataset

output
type: Folder
type: Dataset

Jupyter notebook
date created: 2022-01-28 16:07:32.857476+00:00
date modified: 2023-03-20 16:26:39.058397+00:00
description: Jupyter Notebook hosted by the Environmental Data Science Book
license: no-permission
sd date published: 2022-01-28 16:07:32.857476+00:00
type: Resource
type: MediaObject
type: earth-scienceJupyterNotebook
software requirements: https://w3id.org/ro-id/42a5f00d-7eee-4dbe-85d6-c192fa6e135e/resources/83b2492d-b4bb-4f8c-acc9-775e288a971c

Lock conda file for linux-64
date created: 2022-01-31 11:16:54.901085+00:00
date modified: 2023-03-20 16:26:39.778423+00:00
description: Lock conda file for linux-64 OS of the Jupyter Book hosted by the Environmental Data Science Book
license: no-permission
sd date published: 2022-01-31 11:16:54.901085+00:00
type: Resource
type: MediaObject

Outputs
date created: 2022-01-28 16:07:38.160206+00:00
date modified: 2023-03-20 16:26:43.879157+00:00
description: Contains outputs, (predictions and interactive figure), generated in the Jupyter notebook of Detecting floating objects using deep learning and Sentinel-2 imagery
license: no-permission
sd date published: 2022-01-28 16:07:38.160206+00:00
type: Dataset
type: Resource
type: MediaObject

Pip requirements for lock conda environments
date created: 2022-01-31 11:27:45.283002+00:00
date modified: 2023-03-20 16:26:37.078794+00:00
description: Pip requirements file containing libraries to install after conda lock
encoding format: text/plain
license: no-permission
sd date published: 2022-01-31 11:27:45.283002+00:00
type: Resource
type: MediaObject

Towards detecting floating objects on a global scale with learned spatial features using sentinel 2
date created: 2022-01-28 16:07:43.339740+00:00
date modified: 2023-03-20 16:26:38.898474+00:00
description: Publication with further details of the modelling published in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
license: no-permission
sd date published: 2022-01-28 16:07:43.339740+00:00
type: BibliographicResource
type: Resource
type: MediaObject

Online rendered version of the Jupyter notebook
date created: 2022-01-31 11:16:52.095424+00:00
date modified: 2023-03-20 16:26:43.642918+00:00
description: Rendered version of the Jupyter Notebook hosted by the Environmental Data Science Book
encoding format: text/html
license: no-permission
sd date published: 2022-01-31 11:16:52.095424+00:00
type: Resource
type: MediaObject
type: publication

sketch_680px.png
content size: 1799691
date created: 2023-03-05 21:59:06.519381+00:00
date modified: 2023-03-20 16:26:38.219207+00:00
encoding format: image/png
license: no-permission
sd date published: 2023-03-05 21:59:06.519381+00:00
type: Sketch
type: Resource
type: MediaObject

Lock conda file for osx-64
date created: 2022-01-31 11:16:56.332731+00:00
date modified: 2023-03-20 16:26:36.488124+00:00
description: Lock conda file for osx-64 OS of the Jupyter Book hosted by the Environmental Data Science Book
license: no-permission
sd date published: 2022-01-31 11:16:56.332731+00:00
type: Resource
type: MediaObject

Conda environment
date created: 2022-01-31 11:32:03.379546+00:00
date modified: 2023-03-20 16:26:39.940401+00:00
description: Conda environment when user want to have the same libraries installed without concerns of package versions
license: no-permission
sd date published: 2022-01-31 11:32:03.379546+00:00
type: Resource
type: MediaObject

Detecting macro floating objects on coastal water bodies using sentinel-2 data
date created: 2022-01-28 16:07:40.875698+00:00
date modified: 2023-03-20 16:26:43.758517+00:00
description: Related publication of the modelling published in OCEANS 2021
license: no-permission
sd date published: 2022-01-28 16:07:40.875698+00:00
type: BibliographicResource
type: Resource
type: MediaObject

Input images
date created: 2022-01-28 16:07:34.662177+00:00
date modified: 2023-03-20 16:26:36.244852+00:00
description: Contains input analysis-ready input images used in the Jupyter notebook of Detecting floating objects using deep learning and Sentinel-2 imagery
license: no-permission
sd date published: 2022-01-28 16:07:34.662177+00:00
type: Dataset
type: Resource
type: MediaObject

Sentinel-2
type: Lemma
norm score: 17.521902377972463
score: 14.0

publishing
type: Domain
norm score: 100.0
score: 4.8

research
type: Lemma
norm score: 12.891113892365455
score: 10.3

detection
type: Concept
norm score: 15.395284327323164
score: 11.1

notebook
type: Lemma
norm score: 12.891113892365455
score: 10.3

imagery
type: Concept
norm score: 13.730929264909848
score: 9.9

deep learning
type: Phrase
norm score: 5.391658189216684
score: 5.3

Language
type: IPTC
path: Arts, culture and entertainment/Culture/Language

physical object
type: Concept
norm score: 11.511789181692096
score: 8.3

object
type: Lemma
norm score: 11.76470588235294
score: 9.4

earth sciences
type: FieldOfResearch
norm score: 100.0
score: 0.5625630021095276

learning
type: Concept
norm score: 10.540915395284328
score: 7.6

research object
type: Phrase
norm score: 34.28280773143439
score: 33.7

refer to the detecting
type: Phrase
norm score: 1.7293997965412005
score: 1.7

Literature
type: IPTC
path: Arts, culture and entertainment/Arts and entertainment/Literature

https://w3id.org/ro-id/b34facfa-cea8-48f5-89f6-f11ce00812a/0edc91a9-9049-4357-bad7-677880c8fd8a
as WKT: POLYGON ((26.521543885417145 39.03722381230471, 26.52744474524991 39.03722381230471, 26.52744474524991 39.04105711064335, 26.521543885417145 39.04105711064335, 26.521543885417145 39.03722381230471))
type: Geometry
type: Polygon

https://w3id.org/ro-id/b34facfa-cea8-48f5-89f6-f11ce00812a/19f9ef3f-8678-48f5-a9ac-cf364939dcda
as WKT: POLYGON ((-43.2315509040757 -22.80675019122663, -43.02418396071632 -22.80675019122663, -43.02418396071632 -22.678831998280632, -43.2315509040757 -22.678831998280632, -43.2315509040757 -22.80675019122663))
type: Geometry
type: Polygon

https://w3id.org/ro-id/b34facfa-cea8-48f5-89f6-f11ce00812a/4b55fad1-092b-4657-a004-aafc05499e18
as WKT: POLYGON ((-86.82548387 20.977342054, -86.751891297 20.977342054, -86.751891297 21.033273193, -86.82548387 21.033273193, -86.82548387 20.977342054))
type: Geometry
type: Polygon

https://w3id.org/ro-id/b34facfa-cea8-48f5-89f6-f11ce00812a/e2eba045-3c2c-44a1-aa39-06cc232d05f9
as WKT: POLYGON ((119.12620106576212 39.27393119797617, 119.19563809273966 39.27393119797617, 119.19563809273966 39.30980175207518, 119.12620106576212 39.30980175207518, 119.12620106576212 39.27393119797617))
type: Geometry
type: Polygon

research
type: Concept
norm score: 12.621359223300972
score: 9.1

imagery notebook
type: Phrase
norm score: 43.336724313326556
score: 42.6

geology
type: FieldOfResearch
norm score: 100.0
score: 0.5625630021095276

detecting
type: Lemma
norm score: 15.269086357947433
score: 12.2

The research object refers to the Detecting floating objects using deep learning and Sentinel-2 imagery notebook published in the Environmental Data Science book.
type: Sentence
norm score: 56.45645645645645
score: 56.4

communications and radar
type: NASA
norm score: 100.0
score: 0.5148147940635681

engineering
type: NASA
norm score: 100.0
score: 0.5148147940635681

object
type: Concept
norm score: 9.431345353675452
score: 6.8

Education
type: IPTC
path: Education

Environmental Data Science Book Community
type: Person
type: Agent

Alejandro Coca-Castro
affiliation: The Alan Turing Institute
type: Agent

Jamila Mifdal
affiliation: European Space Agency Φ-lab
type: Agent

Raquel Carmo
affiliation: European Space Agency Φ-lab
type: Agent

sig
has algorithm: RSA
signed by: RoCrateBot