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Pregled bibliografske jedinice broj: 1147659

An assessment of social media oil spill reports using transfer learning on remotely sensed images


Ivanda, Antonia; Mladenović, Saša; Šerić, Ljiljana; Braović, Maja
An assessment of social media oil spill reports using transfer learning on remotely sensed images // SpliTech 2021: 6th International Conference on Smart and Sustainable Technologies: Proceedings / Varbanov, Petar S. ; Lopez de Ipinia Gonzales de Artaza, Diego (ur.).
Split: FESB, University of Split, 2021. str. 1-6 doi:10.23919/SpliTech52315.2021.9566456 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1147659 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
An assessment of social media oil spill reports using transfer learning on remotely sensed images

Autori
Ivanda, Antonia ; Mladenović, Saša ; Šerić, Ljiljana ; Braović, Maja

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
SpliTech 2021: 6th International Conference on Smart and Sustainable Technologies: Proceedings / Varbanov, Petar S. ; Lopez de Ipinia Gonzales de Artaza, Diego - Split : FESB, University of Split, 2021, 1-6

ISBN
978-953-290-111-5

Skup
6th International Conference on Smart and Sustainable Technologies, SpliTech 2021

Mjesto i datum
Bol i Split (Online), Hrvatska, 08-11.09.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Oil spill ; Twitter ; Deep learning ; Transfer learn-ing ; SAR ; Sentinel-1

Sažetak
Nowadays, we are increasingly aware that irrespon- sible human behavior is the main reason for many instances ofenvironmental pollution, including oil spills in the sea. In orderto detect such contaminants in a timely manner and take care ofthem as quickly as possible, in this paper we present a methodfor automatic oil spill detection through remote sensing overSentinel-1 SAR images. Presented approach uses social mediahuman reports of oil spill as input information for building adata set and training a model over collected images of actualoil spills. Because of a rather small oil spill data set, we areusing two methods for obtaining better results. Namely, we usedata set augmentation for enrichment of images in the data setand transfer learning to retrain our collection of images basedon trained deep network on ImageNet data set. For classifyingimages we used four machine learning classification modelswith different accuracy in oil spill detection. A comparison ofaccuracies of the individual machine learning model and thewhole process of oil spill detection initiated by social mediareports and further validated by constructed classifier will bedescribed in this paper.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Projekti:
EK-EFRR-KK.01.1.1.04.0064 - Razvoj tehnologije za procjenu autopurifikacijskih sposobnosti priobalnih voda (CAAT) (Andričević, Roko, EK - KK.01.1.1.04. Ulaganje u znanost i inovacije-Prvi poziv) ( POIROT)

Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split,
Prirodoslovno-matematički fakultet, Split

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Ivanda, Antonia; Mladenović, Saša; Šerić, Ljiljana; Braović, Maja
An assessment of social media oil spill reports using transfer learning on remotely sensed images // SpliTech 2021: 6th International Conference on Smart and Sustainable Technologies: Proceedings / Varbanov, Petar S. ; Lopez de Ipinia Gonzales de Artaza, Diego (ur.).
Split: FESB, University of Split, 2021. str. 1-6 doi:10.23919/SpliTech52315.2021.9566456 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Ivanda, A., Mladenović, S., Šerić, L. & Braović, M. (2021) An assessment of social media oil spill reports using transfer learning on remotely sensed images. U: Varbanov, P. & Lopez de Ipinia Gonzales de Artaza, D. (ur.)SpliTech 2021: 6th International Conference on Smart and Sustainable Technologies: Proceedings doi:10.23919/SpliTech52315.2021.9566456.
@article{article, author = {Ivanda, Antonia and Mladenovi\'{c}, Sa\v{s}a and \v{S}eri\'{c}, Ljiljana and Braovi\'{c}, Maja}, year = {2021}, pages = {1-6}, DOI = {10.23919/SpliTech52315.2021.9566456}, keywords = {Oil spill, Twitter, Deep learning, Transfer learn-ing, SAR, Sentinel-1}, doi = {10.23919/SpliTech52315.2021.9566456}, isbn = {978-953-290-111-5}, title = {An assessment of social media oil spill reports using transfer learning on remotely sensed images}, keyword = {Oil spill, Twitter, Deep learning, Transfer learn-ing, SAR, Sentinel-1}, publisher = {FESB, University of Split}, publisherplace = {Bol i Split (Online), Hrvatska} }
@article{article, author = {Ivanda, Antonia and Mladenovi\'{c}, Sa\v{s}a and \v{S}eri\'{c}, Ljiljana and Braovi\'{c}, Maja}, year = {2021}, pages = {1-6}, DOI = {10.23919/SpliTech52315.2021.9566456}, keywords = {Oil spill, Twitter, Deep learning, Transfer learn-ing, SAR, Sentinel-1}, doi = {10.23919/SpliTech52315.2021.9566456}, isbn = {978-953-290-111-5}, title = {An assessment of social media oil spill reports using transfer learning on remotely sensed images}, keyword = {Oil spill, Twitter, Deep learning, Transfer learn-ing, SAR, Sentinel-1}, publisher = {FESB, University of Split}, publisherplace = {Bol i Split (Online), Hrvatska} }

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