Ocean Observation Technologies: A Review

Lin, Mingwei; Yang, Canjun

doi:10.1186/s10033-020-00449-z

Cited by 74 publications

(26 citation statements)

References 122 publications

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“…Other small coastal observatories are dedicated to the study of biological and ecological processes, including the Bonne Bay Observatory in Newfoundland (Canada) and the OBSEA observatory near Barcelona (Spain) ( Del-Rio et al., 2020 ). As shown in Figure 5 , the typical cabled seafloor observation networks generally include an underwater cable, a shore station control center, sensors, a junction box, biological samplers, and a watertight connector module ( Lin and Yang, 2020 ). The shore station control center acts as the central brain of the network, which is in charge of data transfer and storage, system operation and management, and emergency response.…”

Section: Development Of Deep-sea Equipmentmentioning

confidence: 99%

Role of deep-sea equipment in promoting the forefront of studies on life in extreme environments

et al. 2021

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Summary The deep-sea environment creates the largest ecosystem in the world with the largest biological community and extensive undiscovered biodiversity. Nevertheless, these ecosystems are far from well known. Deep-sea equipment is an indispensable approach to research life in extreme environments in the deep-sea environment because of the difficulty in obtaining access to these unique habitats. This work reviewed the historical development and the state-of-the-art of deep-sea equipment suitable for researching extreme ecosystems, to clarify the role of this equipment as a promoter for the progress of life in extreme environmental studies. Linkages of the developed deep-sea equipment and the discovered species are analyzed in this study. In addition, Equipment associated with researching the deep-sea ecosystems of hydrothermal vents, cold seeps, whale falls, seamounts, and oceanic trenches are introduced and analyzed in detail. To clarify the thrust and key points of the future promotion of life in extreme environmental studies, prospects and challenges related to observing equipment, samplers, laboratory simulation systems, and submersibles are proposed. Furthermore, a blueprint for the integration of in situ observations, sampling, controllable culture, manned experiments in underwater environments, and laboratory simulations is depicted for future studies.

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Section: Development Of Deep-sea Equipmentmentioning

confidence: 99%

Role of deep-sea equipment in promoting the forefront of studies on life in extreme environments

et al. 2021

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“…Earth observation (EO) technologies provide plentiful satellite, aerial, or unmanned aerial vehicle imageries for monitoring environmental dynamics and infrastructure changes in cities. The optical, synthetic aperture radar, laser, and sound-based systems support observations of essential elements of large areas on Earth, such as oceans (Lin and Yang, 2020), forests (Hirschmugl et al, 2017), carbon biomass assessment, food security, water resources, health and air quality (Kansakar and Hossain, 2016), and land use/land cover (Pandey et al, 2021). Local observations, such as vehicle-based camera systems, sensor networks, and the Internet of Things (IoT), are deployed to monitor the people, infrastructure, and things in the surrounding environment within a city (Tu et al, 2020).…”

Section: Observation Data For Smart Citiesmentioning

confidence: 99%

Integrated environmental and human observations for smart cities

Fang

Shaw

Wang

et al. 2021

Environment and Planning B: Urban Analytics and City Science

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The smartness of cities is also linked with the basic needs of human beings and society, such as the physiological needs, the safety needs, the belonging needs, the esteem needs, and the selfactualization needs (Maslow, 1943). Xu and Geng (2019) introduced the notion of people-centric service intelligence for smart cities, which was an attempt to investigate smartness. When setting up physical infrastructures (smart environment and smart mobility), innovative ecosystems (smart people and smart economy), and quality of life (smart governance and smart living) for smart cities, we, therefore, need to rethink the smartness of smart cities, which is appropriate to each city based on its culture, people's needs, economic development, and other factors.

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“…A UTONOMOUS vehicles such as drones in the air and cars on the road will gradually be achieved in the near future, and such a trend towards realising the autonomy has been increasingly endorsed by shipping industries and maritime community [1]. Currently, with the help of advanced sensors such as vision systems, LiDARs and radars, a wide applications of marine autonomy have been witnessed in various domains such as hydrography, oceanography and offshore technologies [2]. However, most of these applications are still using remote control or semi-autonomous navigation.…”

Section: Introductionmentioning

confidence: 99%

WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

Chen

Liu

Achuthan

2021

IEEE Trans. Instrum. Meas.

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A reliable obstacle detection system is crucial for Autonomous Surface Vehicles (ASVs) to realise fully autonomous navigation with no need of human intervention. However, the current detection methods have particular drawbacks such as poor detection for small objects, low estimation accuracy caused by water surface reflection and a high rate of false-positive on water-sky interference. Therefore, we propose a new encoderdecoder structured deep semantic segmentation network, which is Water Obstacle Detection network based on Image Segmentation (WODIS), to solve above mentioned problems. The first design feature of WODIS utilises the use of an encoder network to extract high-level data based on different sampling rates. In order to improve obstacle detection at sea-sky-line areas, an Attention Refine Module (ARM) activated by both global average pooling and max pooling to capture high-level information has been designed and integrated into WODIS. In addition, a Feature Fusion Module (FFM) is introduced to help concatenate the multi-dimensional high-level features in the decoder network. The WODIS is tested and cross validated using four different types of maritime datasets with the results demonstrating that mIoU of WODIS can achieve superior segmentation effects for sea level obstacles to values as high as 91.3%.

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Ocean Observation Technologies: A Review

Cited by 74 publications

References 122 publications

Role of deep-sea equipment in promoting the forefront of studies on life in extreme environments

Role of deep-sea equipment in promoting the forefront of studies on life in extreme environments

Integrated environmental and human observations for smart cities

WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments

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