An Overview of Computer Vision in iNaturalist
iNaturalist is a social network of people who record and share observations of biodiversity. For several years, iNaturalist has been employing computer vision models trained on iNaturalist data to provide automated species identification assistance to iNaturalist participants. This presentation offers an overview of how we are using this technology, the data and tools we used to create it, challenges we have faced in its development, and ways we might apply it in the future.
Er staat Open Access en toch kan ik de file niet downloaden..
https://zenodo.org/record/4074689/files/BISS_article_59133.pdf?download=1
Comentarios
Biodiversity Information Standards TDWG
TDWG 2020 Keynote by Ken-ichi Ueda
Ueda K-i (2020) An Overview of Computer Vision in iNaturalist. Biodiversity Information Science and Standards 4: e59133. https://doi.org/10.3897/biss.4.59133
https://www.youtube.com/watch?v=xfbabznYFV0
Ja, het download nu vanzelf
De grassen en waterplanten hebben geen nederlandse naam
Juncus, eragrotis, potamogeton, lemna
https://www.inaturalist.org/check_lists/4088583-Vaatplanten-without-common-names?q=&view=photo&taxon=59109&observed=any&threatened=any&establishment_means=any&occurrence_status=not_absent&rank=species&taxonomic_status=active
Maar het artikel is niet volledig toch ?
Biodiversity Information Standards TDWG
TDWG 2020 Keynote by Ken-ichi Ueda
Ueda K-i (2020) An Overview of Computer Vision in iNaturalist. Biodiversity Information Science and Standards 4: e59133. https://doi.org/10.3897/biss.4.59133
https://www.youtube.com/watch?v=xfbabznYFV0
Thanks ahospers for finding out the answer:
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Overview of Computer Vision in iNaturalist
Corresponding author: Ken-ichi Ueda (kueda@inaturalist.org)
Received: 29 Sep 2020 | Published: 01 Oct 2020
Citation: Ueda K-i (2020) An Overview of Computer Vision in iNaturalist. Biodiversity Information Science and
Standards 4: e59133. https://doi.org/10.3897/biss.4.59133
--
Abstract
iNaturalist is a social network of people who record and share observations of biodiversity.
For several years, iNaturalist has been employing computer vision models trained on
iNaturalist data to provide automated species identification assistance to iNaturalist
participants. This presentation offers an overview of how we are using this technology, the
data and tools we used to create it, challenges we have faced in its development, and
ways we might apply it in the future.
Presenting author
Ken-ichi Ueda
Presented at
TDWG 2020
It's available here: https://www.youtube.com/watch?v=xfbabznYFV0
https://drentsemusea.nl/hunebedcentrum/lezingen-archeologie-vanuit-microperspectief/
https://cloud.google.com/vision/docs/detecting-landmarks
https://observation.org/download/Biodiv%20Next%20-%20Dutch_Belgian%20species%20ID%20.pptx
https://www.inaturalist.org/journal/ahospers/61053-272-identify-plants-birds-and-insects-in-photos
https://twitter.com/search?q=pypsa Ik dacht dat ergens door betere voorspellingen negatieve prizjen bijna zouden verdwijn maar ik kan he net vindne
https://zenodo.org/record/7050651/files/Naturalis%20Biodiversity%20Center%20%282022%29.%20Eindrapportage%20project%20Automatische%20beeldherkenning%20voor%20museumcollecties.pdf?download=1
Naturalis Biodiversity Center (2022). Eindrapportage project Automatische beeldherkenning voor museumcollecties.pdf
https://www.nlbif.nl/verborgen-biodata/ wist je dit...geleid door gallenman
https://twitter.com/hashtag/EnergyTwitterVrijMiBo?src=hashtag_click
https://www.researchgate.net/publication/341278898_Big_Data_Ja_Natuurlijk
https://www.researchgate.net/publication/349378026_Diet_composition_of_the_golden_jackal_Canis_aureus_in_south-east_Europe_-a_review
https://www.knawonderwijsprijs.nl/bestandenafbeeldingen/2016/modelleren-van-vegetatiepatronen.pdf
https://www.geologienederland.nl/wp-content/uploads/2022/09/NGV_THM_CRB_Themadag_Noordzeebodem_2022_-_Samenvattingen_lezingen_versie_2.pdf
https://www.techthics.nl/wp-content/uploads/2022/02/erdincsacan-inclusieveartificialintelligence.pdf
https://arxiv.org/pdf/1906.10742.pdf
https://arxiv.org/pdf/2103.10703.pdf
https://www.tmgonline.nl/article/10.18146/tmg.815/
https://github.com/joergmlpts
https://zenodo.org/record/7050651#.Y2gH23bMJaT
pagina 11
'Gebruikte software en hardware
Het trainen van modellen in dit project is gedaan met
behulp van TensorFlow, het platform van Google voor
machine learning.5
De code is geschreven in Python 3,
gebruik makend van Keras, een open source-pakket
dat fungeert als interface voor TensorFlow.
Alle preprocessing van de data (downloaden,
bestanden lezen, etc.) is door de bij dit project
betrokken software-ontwikkelaar geschreven in
Python of in Bash-scripts, gebruik makend van
standaard Linux-tools. Dit geldt ook voor analyse en
presentatie van de resultaten.
Het experimenteel geautomatiseerd bijsnijden
van afbeeldingen vond plaats met ImageMagick.
Alle modellen maken gebruik van de InceptionV3-
architectuur. Er zijn tests gedraaid met andere
architecturen, zoals VGG16, ResNet50 en Xception.
Alle getrainde modellen zijn opgesla'
https://imafungus.biomedcentral.com/articles/10.1186/s43008-022-00099-x
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