Althaus, F., Hill, N., Ferrari, R., Edwards, L., Przeslawski, R., Schönberg, C.H., Stuart-Smith, R., Barrett, N., Edgar, G., and Colquhoun, J. 2015. A standardised vocabulary for identifying benthic biota and substrata from underwater imagery: the CATAMI classification scheme. PLoS ONE 10:e0141039.

Barkby, S., Williams, S.B., Pizarro, O., Jakuba, M., 2009. An Efficient Approach to Bathymetric SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems, 219-224.

Beijbom, O., Edmunds, P.J., Kline, D., Mitchell, B.G., Kriegman, D. 2012. Automated Annotation of Coral Reef Survey Images. IEEE Conference on Computer Vision and Pattern Recognition, 1170–77.

Beijbom, O., Treibitz, T., Kline, D.I., Eyal, G., Khen, A., Neal, B., Loya, Y., Mitchell, B.G., Kriegman D. 2016. Improving Automated Annotation of Benthic Survey Images Using Wide-Band Fluorescence. Scientific Reports 6: 23166.

Bewley, M.S., Douillard, B., Nourani-Vatani, N., Friedman, A., Pizarro, O., Williams, S.B. 2012. Automated Species Detection: An Experimental Approach to Kelp Detection from Sea-floor AUV Images. http://www.araa.asn.au/acra/acra2012/papers/pap140.pdf.

Bewley, M.S. Nourani-Vatani, N., Rao, D., Douillard, B., Pizarro, O., Williams, S.B. 2015. Hierarchical Classification in AUV Imagery. L. Mejias, P. Corke, and J. Roberts (eds.), Field and Service Robotics, Springer Tracts in Advanced Robotics 105

Bewley, M., Friedman, A., Ferrari, R., Hill, N., Hovey, R., Barrett, N., Marzinelli, E.M., Pizarro, O., Figueira, W., Meyer, L., Babcock, R., Bellchambers, L., Byrne, M., Williams, S.B., 2015. Australian sea-floor survey data, with images and expert annotations. Scientific Data 2, 150057.

Boutros, N., Shortis, M.R., Harvey, E.S., 2015. A comparison of calibration methods and system configurations of underwater stereo-video systems for applications in marine ecology. Limnology and Oceanography Methods 13, 224-236.

Bridge, T.C.L., Done, T.J., Friedman, A., Beaman, R.J., Williams, S.B., Pizarro, O., Webster, J.M., 2011. Variability in mesophotic coral reef communities along the Great Barrier Reef, Australia. Marine Ecology Progress Series 428, 63-75.

Bryson, M., Johnson-Roberson, M., Pizarro, O., Williams, S.B., 2016. True Color Correction of Autonomous Underwater Vehicle Imagery. Journal of Field Robotics 33, 853-874.

Clarke, M.E., Tolimieri, N., Singh, H., 2009. Using the Seabed AUV to Assess Populations of Groundfish in Untrawlable Areas, in: Beamish, R.J., Rothschild, B.J. (Eds.), The Future of Fisheries Science in North America. Springer Netherlands, Dordrecht, pp. 357-372.

Connelly, D.P., Copley, J.T., Murton, B.J., Stansfield, K., Tyler, P.A., German, C.R., Van Dover, C.L., Amon, D., Furlong, M., Grindlay, N., Hayman, N., Huhnerbach, V., Judge, M., Le Bas, T., McPhail, S., Meier, A., Nakamura, K., Nye, V., Pebody, M., Pedersen, R.B., Plouviez, S., Sands, C., Searle, R.C., Stevenson, P., Taws, S., Wilcox, S., 2012. Hydrothermal vent fields and chemosynthetic biota on the world’s deepest seafloor spreading centre. Nature Communications 3.

Denuelle, A., Dunbabin, M. 2010. Kelp detection in highly dynamic environments using texture recognition. The Australasian Conference on Robotics & Automation.

Durden, J. M., Schoening, T., Althaus, F., Friedman, A., Garcia, R., Glover, A.G., Greinert, J., Stout, N. J., Jones, D.O.B., Jordt, A., Kaeli, J.W., Koser, K., Kuhnz, L.A., Lindsay, D., Morris, K.J., Nattkemper, T.W., Osterloff, J., Ruhl, H.A., Singh, H., Tran M., Bett, B.J., 2016. Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding. Oceanography and Marine Biology: An Annual Review 54, 1-72.

Ferrari, R., Bryson, M., Bridge, T., Hustache, J., Williams, S.B., Byrne, M., Figueira, W., 2016a. Quantifying the response of structural complexity and community composition to environmental change in marine communities. Global Change Biology 22, 1965-1975.

Ferrari, R., McKinnon, D., He, H., Smith, R.N., Corke, P., González-Rivero, M., Mumby, P.J., Upcroft, B., 2016b. Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling. Remote Sensing 8, 113.

Foster, S.D., Hosack, G.R., Hill, N.A., Barrett, N.S., Lucieer, V.L., 2014. Choosing between strategies for designing surveys: autonomous underwater vehicles. Methods Ecology and Evolution 5, 287-297.

Foster, S.D., Hosack, G.R., Lawrence, E., Przeslawski, R., Hedge, P., Caley, M.J., Barrett, N.S., Williams, A., Li, J., Lynch, T., Dambacher, J.M., Sweatman, H.P.A., Hayes, K.R., 2017. Spatially balanced designs that incorporate legacy sites. Methods in Ecology and Evolution, 8, 1433-1442.

Friedman, A., Steinberg, D., Pizarro, O., Williams, S.B., 2011. Active Learning Using a Variational Dirichlet Process Model for Pre-Clustering and Classification of Underwater Stereo Imagery. IEEE/RSJ International Conference on Intelligent Robots and Systems, 1533–1539.

Furlong, M.E., Paxton, D., Stevenson, P., Pebody, M., McPhail, S.D., Perrett, J., 2012. Autosub Long Range: A Long Range Deep Diving AUV for Ocean Monitoring. IEEE/OES Autonomous Underwater Vehicles (AUV), 1-7.

Hill, N.A., Lucieer, V., Barrett, N.S., Anderson, T.J., Williams, S.B., 2014. Filling the gaps: Predicting the distribution of temperate reef biota using high resolution biological and acoustic data. Estuarine and Coastal Shelf Science 147, 137-147.

Hobson, B.W., Bellingham, J.G., Kieft, B., McEwen, R., Godin, M., Zhang, Y., 2012. Tethys-class long range AUVs - extending the endurance of propeller-driven cruising AUVs from days to weeks. IEEE/OES Autonomous Underwater Vehicles (AUV) 1-8.

Huvenne, V.A.I., McPhail, S.D., Wynn, R.B., Furlong, M., Stevenson, P., 2009. Mapping Giant Scours in the Deep Ocean. Eos, Transactions American Geophysical Union 90, 274-275.

Huvenne, V.A.I., Robert, K., Marsh, L., Lo Iacono, C., Le Bas, T., Wynn, R.B., 2018. “ROVs and AUVs.” In Submarine Geomorphology, edited by A. Micallef, S. Krastel, and A. Savini, 572. Springer Geology. Cham, Switzerland: Springer.

James, L.C., Marzloff, M.P., Barrett, N., Friedman, A., Johnson, C.R., 2017. Changes in deep reef benthic community composition across a latitudinal and environmental gradient in temperate Eastern Australia. Marine Ecology Progress Series 565, 35-52.

Ling, S.D., Mahon, I., Marzloff, M.P., Pizarro, O., Johnson, C.R., Williams, S.B., 2016. Stereo-imaging AUV detects trends in sea urchin abundance on deep overgrazed reefs. Limnology and Oceanography Methods 14, 293-304.

Lucieer, V., Hill, N.A., Barrett, N.S., Nichol, S., 2013. Do marine substrates ‘look’ and ‘sound’ the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images. Estuarine and Coastal Shelf Science 117, 94-106.

Mahmood, A., Bennamoun, M., An, S., Sohel, F., Boussaid, F., Hovey, R., Kendrick, G., Fisher, R 2016. Automatic annotation of coral reefs using deep learning, IEEE OCEANS Monterey, 1–5.

Mahmood, A., Bennamoun, M., An, S., Sohel, F. 2016. Resfeats: Residual network based features for image classification, arXiv preprint arXiv:1611.06656.

Marcos, M.S.A., Soriano, M., Saloma, C. 2005. Classification of Coral Reef Images from Underwater Video Using Neural Networks. Optics Express 13: 8766–71.

Marzinelli, E.M., Williams, S.B., Babcock, R.C., Barrett, N.S., Johnson, C.R., Jordan, A., Kendrick, G.A., Pizarro, O.R., Smale, D.A., Steinberg, P.D., 2015. Large-scale geographic variation in distribution and abundance of Australian deep-water kelp forests. PLoS One 10, e0118390.

Milligan, R., K. Morris, B. Bett, J. Durden, D. Jones, K. Robert, H. Ruhl & D. Bailey, 2016. High resolution study of the spatial distributions of abyssal fishes by autonomous underwater vehicle. Scientific Reports 6:26095 doi:10.1038/srep26095.

Mitchell, PJ., Monk, J., Laurenson, L. 2017. Sensitivity of Fine-Scale Species Distribution Models to Locational Uncertainty in Occurrence Data across Multiple Sample Sizes. Methods in Ecology and Evolution. 8:12-21.

Monk, J., Barrett, N.S., Hill, N.A., Lucieer, V.L., Nichol, S.L., Siwabessy, P.J.W., Williams, S.B., 2016. Outcropping reef ledges drive patterns of epibenthic assemblage diversity on cross-shelf habitats. Biodiversity and Conservation 25, 485-502.

Morris, K., B. Bett, J. Durden, V. Huvenne, R. Milligan, D. Jones, S. McPhail, K. Robert, D. Bailey & H. Ruhl, 2014. A new method for ecological surveying of the abyss using autonomous underwater vehicle photography. Limnology and Oceanography: Methods 12:795-809.

Morris, K., B. Bett, J. Durden, N. Benoist, V. Huvenne, D. Jones, K. Robert, M. Ichino, G. Wolff & H. Ruhl, 2016. Landscape-scale spatial heterogeneity in phytodetrital cover and megafauna biomass in the abyss links to modest topographic variation. Scientific Reports 6:34080 doi:doi:10.1038/srep34080.

Palomer, A., Ridao, P., Ribas, D., Mallios, A., Vallicrosa, G., 2013. A Comparison of G2o Graph SLAM and EKF Pose Based SLAM with Bathymetry Grids. IFAC Proceedings Volumes 46, 286-291.

Pennington, J.T., Blum, M., Chavez, F.P., 2016. Seawater sampling by an autonomous underwater vehicle: “Gulper” sample validation for nitrate, chlorophyll, phytoplankton, and primary production. Limnology and Oceanography: Methods 14, 14-23.

Perkins, N.R., Foster, S.D., Hill, N.A., Barrett, N.S., 2016. Image subsampling and point scoring approaches for large-scale marine benthic monitoring programs. Estuarine and Coastal Shelf Science 76, 36-46.

Perkins, N.R., Foster, S.D., Hill, N.A., Marzloff, M.P., Barrett, N.S., 2017. Temporal and spatial variability in the cover of deep reef species: Implications for monitoring. Ecological Indicators 77, 337-347.

Perkins, N.R., Hill, N.A., Foster, S.D., Barrett, N.S., 2015. Altered niche of an ecologically significant urchin species, Centrostephanus rodgersii, in its extended range revealed using an Autonomous Underwater Vehicle. Estuarine and Coastal Shelf Science 155, 56-65.

Pizarro, O., Rigby, P., Johnson-Roberson, M., Williams, S.B., Colquhoun, J. 2008. Towards image-based marine habitat classification. IEEE explore OCEANS 1–7.

Pizarro, O., Friedman, A., Bryson, M., Williams, S.B., Madin, J., 2017. A simple, fast, and repeatable survey method for underwater visual 3D benthic mapping and monitoring. Ecology and Evolution 7, 1770-1782.

Przeslawski, R., Foster, S., Monk, J., Langlois, T., Lucieer, V., Stuart-Smith, R. 2018. Comparative Assessment of Seafloor Sampling Platforms. Report to the National Environmental Science Programme, Marine Biodiversity Hub. Geoscience Australia. 57 pp_._

Rigaud, V., 2007. Innovation and operation with robotized underwater systems. Journal of Field Robotics 24, 449-459.

Roelfsema, C., Phinn, S., Joyce, K., 2006. Evaluating benthic survey techniques for validating maps of coral reefs derived from remotely sensed images 10th International Coral Reef Symposium, pp. 177-1780.

Seiler, J., Williams, A., Barrett, N., 2012. Assessing size, abundance and habitat preferences of the Ocean Perch Helicolenus percoides using a AUV-borne stereo camera system. Fisheries Research 129-130, 64-72.

Smale, D.A., Kendrick, G.A., Harvey, E.S., Langlois, T.J., Hovey, R.K., Van Niel, K.P., Waddington, K.I., Bellchambers, L.M., Pember, M.B., Babcock, R.C., Vanderklift, M.A., Thomson, D.P., Jakuba, M.V., Pizarro, O., Williams, S.B., 2012. Regional-scale benthic monitoring for ecosystem-based fisheries management (EBFM) using an autonomous underwater vehicle (AUV). ICES Journal of Marine Science 69, 1108-1118.

Smith, A.N.H., Anderson, M.J., Pawley, M.D.M., 2017. Could ecologists be more random? Straightforward alternatives to haphazard spatial sampling. Ecography 40, 1251–1255.

Stokes, M.D., Deane G.B. 2009. Automated Processing of Coral Reef Benthic Images. Limnology and Oceanography, Methods / ASLO 7: 157–68.

Sumner, E.J., Peakall, J., Parsons, D.R., Wynn, R.B., Darby, S.E., Dorrell, R.M., McPhail, S.D., Perrett, J., Webb, A., White, D., 2013. First direct measurements of hydraulic jumps in an active submarine density current. Geophysical Research Letters 40, 5904-5908.

Tivey, M.A., Johnson, H.P., Bradley, A., Yoerger, D., 1998. Thickness of a submarine lava flow determined from near-bottom magnetic field mapping by autonomous underwater vehicle. Geophysical Research Letters 25, 805-808.

Van Rein, H., Schoeman, D.S., Brown, C.J., Quinn, R., Breen, J., 2011. Development of benthic monitoring methods using photoquadrats and scuba on heterogeneous hard-substrata: a boulder-slope community case study. Aquatic Conservation 21, 676-689.

Wagner, J.K.S., McEntee, M.H., Brothers, L.L., German, C.R., Kaiser, C.L., Yoerger, D.R., Van Dover, C.L., 2013. Cold-seep habitat mapping: High-resolution spatial characterization of the Blake Ridge Diapir seep field. Deep Sea Research Part 2 Topical Studies in Oceanography 92, 183-188.

Williams, S., Pizarro, O., Jakuba, M., Johnson, C., Barrett, N., Babcock, R., Kendrick, G., Steinberg, P., Heyward, A., Doherty, P., Mahon, I., Johnson-Roberson, M., Steinberg, D., Friedman, A., 2012. Monitoring of Benthic Reference Sites: Using an Autonomous Underwater Vehicle. IEEE Robotic Automation Magazine 19, 73-84.

Williams, S.B., Pizarro, O., Steinberg, D.M., Friedman, A., Bryson, M., 2016. Reflections on a decade of autonomous underwater vehicles operations for marine survey at the Australian Centre for Field Robotics. Annual Reviews in Control 42, 158-165.

Williams, S.B., Pizarro, O., Webster, J.M., Beaman, R.J., Mahon, I., Johnson-Roberson, M., Bridge, T.C.L., 2010. Autonomous underwater vehicle–assisted surveying of drowned reefs on the shelf edge of the Great Barrier Reef, Australia. Journal of Field Robotics 27, 675-697.

Wynn, R.B., Huvenne, V.A.I., Le Bas, T.P., Murton, B.J., Connelly, D.P., Bett, B.J., Ruhl, H.A., Morris, K.J., Peakall, J., Parsons, D.R., Sumner, E.J., Darby, S.E., Dorrell, R.M., Hunt, J.E., 2014. Autonomous Underwater Vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience. Marine Geology 352, 451-468.