Results 11 to 20 of about 30,195 (348)

An integrated approach for the benthic habitat mapping based on innovative surveying technologies and ecosystem functioning measurements. [PDF]

Sci Rep
Piazzolla D, Scanu S, Mancuso FP, Bosch-Belmar M, Bonamano S, Madonia A, Scagnoli E, Tantillo MF, Russi M, Savini A, Fersini G, Sarà G, Coppini G, Marcelli M, Piermattei V.   +14 more
europepmc   +3 more sources

Benthic habitat mapping using UAV photogrammetry and machine learning algorithms

Traditional methods for marine habitat mapping are often time-consuming and rely on sparse sampling. To overcome these limitations, a new method has been developed for large-scale mapping of benthic marine habitats. By deploying several synchronized autonomous underwater drones and using AI, this new approach enables large-scale, continuous, and high ...
Laura Huguenin, Quentin Chenevier, Anthony Sladen, Antoine Eceiza, Frédéric Mittaine   +4 more
openalex   +2 more sources

Guidance for benthic habitat mapping: an aerial photographic approach [PDF]

, 2001
This document, Guidance for Benthic Habitat Mapping: An Aerial Photographic Approach, describes proven technology that can be applied in an operational manner by state-level scientists and resource managers. This information is based on the experience gained by NOAA Coastal Services Center staff and state-level cooperators in the production of a series
Finkbeiner, Mark, Stevenson, Bill, Seaman, Renee   +2 more
openaire   +3 more sources

Comparing convolutional neural network and random forest for benthic habitat mapping in Apollo Marine Park [PDF]

Remote Sensing in Ecology and Conservation, EarlyView.
A comparison of Convolutional Neural Network (CNN) and Random Forest (RF) model predictions of benthic habitats within Apollo Marine Park. The CNN (left) and RF (right) classification maps show the spatial distribution of three habitat types: high energy circalittoral rock with seabed‐covering sponges, low complexity circalittoral rock with non‐crowded
Henry C. Simmons, Dang Nguyen, Benjamin Misiuk, Daniel Ierodiaconou, Sunil Gupta, Oli Dalby, Mary Young   +6 more
openalex   +2 more sources

Towards Adaptive Benthic Habitat Mapping [PDF]

2020 IEEE International Conference on Robotics and Automation (ICRA), 2020
To be published in ICRA2020 conference proceedings.
Shields, Jackson, Pizarro, Oscar, Williams, Stefan Bernard   +2 more
openaire   +4 more sources

Klasifikasi Habitat Bentik Berdasarkan Citra Sentinel-2 di Kepulauan Kei, Maluku Tenggara

Jurnal Ilmu Pertanian Indonesia, 2022
Imagery classification has long been used in analyzing remote sensing data. The use of the classification algorithm model can affect the results in interpreting benthic habitats in shallow water.
La Ode Alifatri, Bayu Prayudha, Kasih Anggraini   +2 more
doaj   +1 more source

Assessing the use of harmonized multisource backscatter data for thematic benthic habitat mapping

Science of Remote Sensing, 2021
Legacy seabed mapping datasets are increasingly common as the need for detailed seabed information is recognized. Acoustic backscatter data from multibeam echosounders can be a useful surrogate for seabed properties and are commonly used for benthic ...
Benjamin Misiuk, Myriam Lacharité, Craig J. Brown   +2 more
doaj   +1 more source

Semiautomated Mapping of Benthic Habitats and Seagrass Species Using a Convolutional Neural Network Framework in Shallow Water Environments

Remote Sensing, 2020
Benthic habitats are structurally complex and ecologically diverse ecosystems that are severely vulnerable to human stressors. Consequently, marine habitats must be mapped and monitored to provide the information necessary to understand ecological ...
Hassan Mohamed, Kazuo Nadaoka, Takashi Nakamura   +2 more
doaj   +1 more source

Automatic Semantic Segmentation of Benthic Habitats Using Images from Towed Underwater Camera in a Complex Shallow Water Environment

Remote Sensing, 2022
Underwater image segmentation is useful for benthic habitat mapping and monitoring; however, manual annotation is time-consuming and tedious. We propose automated segmentation of benthic habitats using unsupervised semantic algorithms.
Hassan Mohamed, Kazuo Nadaoka, Takashi Nakamura   +2 more
doaj   +1 more source

Benthic habitat mapping with autonomous underwater vehicles [PDF]

OCEANS 2008, 2008
We provide an outline of an autonomous benthic habitat mapping algorithm. This algorithm enables real-time on-board classification of images gathered by an autonomous underwater vehicle (AUV), with the ability to classify aquatic vegetation at a resolution approaching the species level.
Andrew Davie, Klaas Hartmann, Greg Timms, Martin de Groot, John McCulloch   +4 more
openaire   +1 more source