000099714 001__ 99714
000099714 005__ 20230519145502.0
000099714 0247_ $$2doi$$a10.3390/rs13040659
000099714 0248_ $$2sideral$$a123213
000099714 037__ $$aART-2021-123213
000099714 041__ $$aeng
000099714 100__ $$aYuval, M.
000099714 245__ $$aRepeatable semantic reef-mapping through photogrammetry and label-augmentation
000099714 260__ $$c2021
000099714 5060_ $$aAccess copy available to the general public$$fUnrestricted
000099714 5203_ $$aIn an endeavor to study natural systems at multiple spatial and taxonomic resolutions, there is an urgent need for automated, high-throughput frameworks that can handle plethora of information. The coalescence of remote-sensing, computer-vision, and deep-learning elicits a new era in ecological research. However, in complex systems, such as marine-benthic habitats, key ecological processes still remain enigmatic due to the lack of cross-scale automated approaches (mms to kms) for community structure analysis. We address this gap by working towards scalable and comprehensive photogrammetric surveys, tackling the profound challenges of full semantic segmentation and 3D grid definition. Full semantic segmentation (where every pixel is classified) is extremely labour-intensive and difficult to achieve using manual labeling. We propose using label-augmentation, i.e., propagation of sparse manual labels, to accelerate the task of full segmentation of photomosaics. Photomosaics are synthetic images generated from a projected point-of-view of a 3D model. In the lack of navigation sensors (e.g., a diver-held camera), it is difficult to repeatably determine the slope-angle of a 3D map. We show this is especially important in complex topographical settings, prevalent in coral-reefs. Specifically, we evaluate our approach on benthic habitats, in three different environments in the challenging underwater domain. Our approach for label-augmentation shows human-level accuracy in full segmentation of photomosaics using labeling as sparse as 0.1%, evaluated on several ecological measures. Moreover, we found that grid definition using a leveler improves the consistency in community-metrics obtained due to occlusions and topology (angle and distance between objects), and that we were able to standardise the 3D transformation with two percent error in size measurements. By significantly easing the annotation process for full segmentation and standardizing the 3D grid definition we present a semantic mapping methodology enabling change-detection, which is practical, swift, and cost-effective. Our workflow enables repeatable surveys without permanent markers and specialized mapping gear, useful for research and monitoring, and our code is available online. Additionally, we release the Benthos data-set, fully manually labeled photomosaics from three oceanic environments with over 4500 segmented objects useful for research in computer-vision and marine ecology.
000099714 536__ $$9info:eu-repo/grantAgreement/ES/MICIU-AEI-FEDER/PGC2018-098817-A-I00
000099714 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000099714 590__ $$a5.349$$b2021
000099714 592__ $$a1.283$$b2021
000099714 594__ $$a7.4$$b2021
000099714 591__ $$aGEOSCIENCES, MULTIDISCIPLINARY$$b30 / 203 = 0.148$$c2021$$dQ1$$eT1
000099714 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2021$$dQ1
000099714 591__ $$aIMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY$$b6 / 28 = 0.214$$c2021$$dQ1$$eT1
000099714 591__ $$aREMOTE SENSING$$b11 / 34 = 0.324$$c2021$$dQ2$$eT1
000099714 591__ $$aENVIRONMENTAL SCIENCES$$b83 / 279 = 0.297$$c2021$$dQ2$$eT1
000099714 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000099714 700__ $$0(orcid)0000-0003-4638-4655$$aAlonso, I.$$uUniversidad de Zaragoza
000099714 700__ $$aEyal, G.
000099714 700__ $$aTchernov, D.
000099714 700__ $$aLoya, Y.
000099714 700__ $$0(orcid)0000-0002-7580-9037$$aMurillo, A.C.$$uUniversidad de Zaragoza
000099714 700__ $$aTreibitz, T.
000099714 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000099714 773__ $$g13, 4 (2021), 659 [19 pp]$$pRemote sens. (Basel)$$tRemote Sensing$$x2072-4292
000099714 8564_ $$s938765$$uhttps://zaguan.unizar.es/record/99714/files/texto_completo.pdf$$yVersión publicada
000099714 8564_ $$s2723048$$uhttps://zaguan.unizar.es/record/99714/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000099714 909CO $$ooai:zaguan.unizar.es:99714$$particulos$$pdriver
000099714 951__ $$a2023-05-18-14:59:13
000099714 980__ $$aARTICLE