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Article

Repeatable Semantic Reef-Mapping through Photogrammetry and Label-Augmentation

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Hatter Department of Marine Technologies, Charney School of Marine Sciences, University of Haifa, Haifa 3498838 , Israel
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Department of Marine Biology, Charney School of Marine Sciences, University of Haifa, 3498838 Haifa, Israel
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Aragón Institute for Engineering Research (I3A), University of Zaragoza, 50009 Zaragoza, Spain
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ARC Centre of Excellence for Coral Reef Studies, School of Biological Sciences, The University of Queensland, Douglas, QLD 4814, Australia
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The Mina & Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan 5290002, Israel
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School of Zoology, Tel-Aviv University, Tel Aviv 6997801, Israel
*
Author to whom correspondence should be addressed.
Current address: Hatter Department of Marine Technologies & Morris Kahn Marine Research Station, Charney School of Marine Sciences, University of Haifa, Haifa 3498838, Israel.
Remote Sens. 2021, 13(4), 659; https://doi.org/10.3390/rs13040659
Received: 19 January 2021 / Revised: 5 February 2021 / Accepted: 9 February 2021 / Published: 11 February 2021
(This article belongs to the Section Coral Reefs Remote Sensing)
In 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. View Full-Text
Keywords: photogrammetry; orthorectification; change-detection; community ecology; label-augmentation; coral-reefs; benthic mapping; computer-vision; multi-level superpixels photogrammetry; orthorectification; change-detection; community ecology; label-augmentation; coral-reefs; benthic mapping; computer-vision; multi-level superpixels
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MDPI and ACS Style

Yuval, M.; Alonso, I.; Eyal, G.; Tchernov, D.; Loya, Y.; Murillo, A.C.; Treibitz, T. Repeatable Semantic Reef-Mapping through Photogrammetry and Label-Augmentation. Remote Sens. 2021, 13, 659. https://doi.org/10.3390/rs13040659

AMA Style

Yuval M, Alonso I, Eyal G, Tchernov D, Loya Y, Murillo AC, Treibitz T. Repeatable Semantic Reef-Mapping through Photogrammetry and Label-Augmentation. Remote Sensing. 2021; 13(4):659. https://doi.org/10.3390/rs13040659

Chicago/Turabian Style

Yuval, Matan, Iñigo Alonso, Gal Eyal, Dan Tchernov, Yossi Loya, Ana C. Murillo, and Tali Treibitz. 2021. "Repeatable Semantic Reef-Mapping through Photogrammetry and Label-Augmentation" Remote Sensing 13, no. 4: 659. https://doi.org/10.3390/rs13040659

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