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The rapid expansion of global coastal urban agglomerations is exerting dual pressures of ecosystem degradation
and resource use conflicts on nearshore environments. Traditional fishing equipment and extensive management
paradigms are increasingly inadequate in addressing the highly complex and dynamically changing urban marine
areas. Although smart technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) present
revolutionary opportunities for upgrading fishing gear, current research predominantly focuses on technical
performance itself, lacking a systemic perspective that places these technologies within the overall governance
framework of urban marine ecosystems. To bridge this gap between technological development and systemic
governance, this paper introduces the theoretical framework of urban oceanography. We propose an innovative
Sensing Decision-Action conceptual model, elucidating how intelligent fishing equipment can transcend its
traditional role, evolving into the real-time sensory nerves of the urban marine environment, the precise executive
tools for management strategies, and the core data engine for scientific decision-making. This paper
systematically reviews technological frontiers such as smart fishing gear, vessel energy management, and remote
sensing detection. Furthermore, it outlines a future-oriented, cross-disciplinary research agenda encompassing
the development of low-cost sensors, the design of data-sharing mechanisms, and policy incentives for
technology adoption. The research demonstrates that deeply integrating intelligent fishing equipment into the
practice of urban oceanography is a critical pathway for constructing a resilient, efficient, and sustainable urban
marine resource management system.
—urban oceanography; intelligent fishing equipment; sensing-decision-action framework;
sustainable governance; marine resource management
Global populations are urbanizing rapidly, with a significant migration towards coastlines, a trend often
supported by policy reforms that promote densification [1]. Worldwide, the concentration of people and
economic activities in coastal areas demonstrates a distinct seaward trend. Population density in coastal zones
(areas within 100 kilometers of the ocean and less than 100 meters above sea level) is approximately three times
the global average and continues to increase [2]. It is estimated that over onethird of the global population lives
within 100 kilometers of a coastline, making nearshore environments the ecological frontlines most frequently
and intensely disturbed by human activities [3]. Most of this population is concentrated in coastal cities, which
often locate at river-sea junctions, serving as trade hubs or situated on fertile deltas [4]. Many of these urban
agglomerations have grown into megacities with populations exceeding ten million [5]. While this process fuels
a prosperous "blue economy", it simultaneously imposes unprecedented environmental stress on the underlying
marine base. Land-based pollutants, including urban sewage, industrial wastewater, and plastic debris, are
continuously discharged into coastal waters via rivers and atmospheric deposition, leading to eutrophication, the
expansion of oxygen-deficient zones, and a sharp decline in biodiversity. For ecologists, coastal cities hold
particular research and conservation significance, not only from a terrestrial perspective but also regarding their
impact on and interaction with the marine environment [6]. Understanding of the effects of urbanization on
marine ecosystems and ecological processes is continuously deepening. Human density is closely linked to
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resource exploitation, and an early impact of marine urbanization is the depletion of nearby fishery resources
[7], [8].
Concurrently, large-scale coastal engineering, land reclamation, and shipping activities directly alter natural
hydrodynamic conditions and habitat structures, fragmenting original ecosystems [9]. Furthermore, intense
competition for marine living resources—encompassing fishing, aquaculture, maritime recreation, and
ecological conservation— makes the management of urban marine spaces exceptionally complex and
challenging [10]. Coastal cities contribute significantly to marine pollution, introducing harmful chemicals,
bacteria, and sediments associated with sewage and urban runoff [11], [12]. They also drive nearshore
development, often starting with ports and including hardened coastal defense structures to reduce erosion of
valuable land, whether original or reclaimed [13]. These artificial structures have profound ecological impacts
on shorelines, especially when natural habitats are replaced entirely by novel materials like concrete and granite
[14]. These activities, infrastructures, and issues represent instances of the overlapping and interacting drivers of
marine urbanization: resource exploitation, ocean sprawl, and pollution pathways [9], [10], [13]. Consequently,
traditional oceanographic research paradigms, which focus on open oceans or single species, are increasingly
inadequate for addressing the challenges posed by the intense, multi-dimensional human activities that dominate
the "Urban Ocean" system.
As one of the industries that is most closely linked to the urban ocean, fisheries face severe survival and
development challenges in this context. Traditional fishing equipment and technological systems were primarily
designed to maximize catch efficiency, with little consideration given to their long-term ecological impacts [15].
Their drawbacks are increasingly evident: Firstly, in terms of efficiency, reliance on experience-based, extensive
fishing practices leads to high fuel consumption and production volatility due to inaccurate knowledge of fishing
grounds. Secondly, regarding environmental protection, non-selective gear often results in significant bycatch of
non-target species (including endangered animals), while practices like bottom trawling cause persistent damage
to seabed habitats. Finally, concerning safety and management, traditional fishing vessels often lack advanced
navigation and communication equipment, increasing operational risks and creating significant difficulties for
government oversight and law enforcement [16].
However, challenges also breed opportunities for transformation. The achievements of the Fourth Industrial
Revolution—represented by IoT, big data, AI, and automation—are rapidly permeating various sectors and
provide powerful technological tools for modernizing fisheries [17]. Intelligent fishing equipment, such as
sensor-equipped gear, smart vessels capable of route optimization, and precise fishing ground forecasting
systems based on remote sensing, demonstrates great potential for addressing the aforementioned dilemmas [18].
The intelligent transformation of fishing equipment has evolved from a forward-looking concept into an
inevitable choice for tackling urban ocean management challenges and achieving sustainable industrial
development.
Current academic discourse and technological research and development on intelligent fishing equipment are
burgeoning but exhibit a significant limitation: much of the research focuses narrowly on improving the technical
parameters of the equipment itself, such as sensor accuracy, algorithm efficiency, or the level of automation.
While this work is important, it often occurs in a "technological vacuum," failing to situate intelligent equipment
within the complex and dynamic macro-scale socialecological system of the "Urban Ocean" it is ultimately meant
to serve. A clear gap exists between technological development and ecosystem management practice.
Specifically, few studies delve deeply into how intelligent equipment can be thoroughly integrated with urban
marine spatial planning, pollution control, biodiversity conservation, and comprehensive management policies.
The global community is increasingly recognizing the need for innovative approaches to ocean sustainability.
International initiatives such as the FAO's vision for Smart Fisheries, which emphasizes the use of digital
technologies for improved monitoring, control, and surveillance—and the UN Decade of Ocean Science for
Sustainable Development (2021-2030), which calls for transformative science and solutions for a healthy ocean,
provide a robust international context for this research [28], [29]. These programs underscore the critical
importance of interdisciplinary knowledge, technological innovation, and data-driven governance, aligning
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closely with the objectives of this study. However, a specific, operational framework that seamlessly integrates
these elements within the unique context of the urban ocean remains underdeveloped.
To fill this research gap, this paper aims to introduce the holistic perspective of urban oceanography to re-
examine and reposition the role of intelligent fishing equipment. Urban oceanography emphasizes a
comprehensive understanding of the complex "city-ocean" coupled system, recognizing human activities as the
key driver shaping nearshore environmental change [19]. From this perspective, this paper redefines intelligent
fishing equipment not merely as isolated "production tools" but as organic, interactive "governance nodes" within
the urban marine ecosystem.
The core innovation of this paper lies in proposing a Sensing-Decision-Action conceptual framework. This
framework systematically elaborates how intelligent equipment can serve as "sensing organs" to collect real-
time marine environmental data, function as an extension of the "decision-making brain" to provide analytical
support for management, and ultimately act as "execution means" to accurately translate management strategies
into environmentally friendly production actions. Through this framework, we strive to build a bridge connecting
technological innovation and systemic governance, providing a novel, cross-disciplinary theoretical foundation
and action roadmap for promoting the sustainability transition of the urban ocean.
With the accelerating pace of global urbanization, coastal zones have become interfaces of intense human activity
and dramatic ecological change. While traditional oceanography has largely focused on natural dynamical
processes and ecosystems, the Anthropocene has witnessed the emergence of a new interdisciplinary field –
Urban Oceanography. Its core premise posits that cities and oceans should not be viewed as independent systems,
but rather as a highly coupled, dynamically interacting "social-ecological system" studied holistically (Figure 1)
[18]. This discipline emphasizes that human activities have superseded natural factors as the primary driver of
environmental change in coastal seas [19]. This driving force is realized through complex exchanges of materials,
energy, and information: cities input pollutants, nutrients, sediments, and heat into the ocean, while
simultaneously extracting living and non-living resources; the ocean, in turn, provides feedback and imposes
constraints on urban development through processes like sea-level rise and storm surges [19]. Understanding
this bidirectional, non-linear coupling relationship is the scientific foundation for addressing the sustainable
development challenges of coastal cities.
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Building upon this core concept, urban oceanography focuses on a series of key scientific questions directly
relevant to coastal environmental health and resource sustainability. Firstly, the transport pathways and
ecological effects of landbased pollutants constitute a central issue. Urban runoff, combined with industrial and
municipal wastewater, forms a complex mixture of contaminants, including heavy metals (e.g., copper, lead,
cadmium), persistent organic pollutants (e.g., PCBs, PBDEs), contaminants of emerging concern (e.g.,
pharmaceuticals, microplastics), and excess nutrients (nitrogen, phosphorus). After entering the marine
environment via rivers, outfalls, or atmospheric deposition, the transport, transformation, fate, and cascading
effects of these pollutants on biological individuals (e.g., physiological toxicity, reproductive inhibition),
populations (loss of genetic diversity), and communities (structural simplification, functional degradation) are
critical for assessing the environmental carrying capacity of the urban ocean. Secondly, coastline artificialization
triggered by "ocean sprawl" and its ecological impacts represents another major research theme [20]. Gray
infrastructure such as seawalls, breakwaters, piers, and land reclamation massively replace natural mudflats,
mangroves, and salt marshes, not only directly causing habitat loss and fragmentation but also profoundly
altering local hydrodynamics, sediment transport patterns, and biogeochemical cycles [14]. These artificial
structures typically feature steeper slopes, simpler surface textures, and fewer microhabitats (e.g., tidal pools,
crevices), consequently supporting ecological communities that differ significantly from natural ecosystems in
species composition, biodiversity, and ecosystem function [21]. Thirdly, the quantitative assessment of urban
marine resources and ecological carrying capacity presents a comprehensive challenge. It requires scientifically
defining the thresholds at which ecosystems can maintain their key functions (e.g., biological production, water
purification, hazard buffering) and provide sustainable ecosystem services (e.g., fishery supply, cultural
recreation) under the pressure of multiple interacting stressors (e.g., overfishing, pollution, physical
modification, climate change). This provides crucial scientific basis for ecosystem-based adaptive management.
The perspective of urban oceanography yields transformative implications for the design, functionality, and
application paradigms of intelligent fishing equipment. It demands a fundamental shift in design philosophy,
moving beyond the traditional role of equipment as mere "fishing tools" and redefining them as intelligent nodes
and ecological infrastructure for understanding, monitoring, and responding to the dynamics of the urban marine
ecosystem. Specifically, the implications manifest at three levels: First, as Sensing Organs. Intelligent fishing
equipment (e.g., smart vessels, unmanned surface vehicles, sensorintegrated gear, or aquaculture platforms)
should function as mobile monitoring stations deployed at sea, continuously collecting high-resolution in-situ
data. This includes physico-chemical water parameters (temperature, salinity, dissolved oxygen, pH, specific
pollutant concentrations), biological information (monitoring species distribution and abundance via underwater
imagery or environmental DNA techniques), and hydrodynamic data (current velocity, waves) [22]. These data
collectively form the "sensory nerve endings" that map the spatiotemporal heterogeneity of urban stressors,
providing the foundational data for understanding the coupled system's mechanics. Second, as Decision-Support
Engines. Leveraging IoT, edge computing, and cloud technologies, the multi-source heterogeneous data collected
by the equipment can be fused, assimilated, and intelligently analyzed to build digital twins or various predictive
models. This enables early warning of environmental anomalies (e.g., harmful algal blooms, hypoxia events),
accurate assessment and prediction of fishery resource dynamics, and provides scientific decision support for
marine spatial planning, pollution control, and fisheries management, thereby enhancing the foresight and
precision of governance. Third, as Eco-engineering Interventions [25]. The equipment's design can proactively
incorporate ecological principles. For instance, its structures can employ eco-friendly materials or be designed
with complex ecological niches to provide habitat for organisms; its operational behavior can be optimized via
AI algorithms to achieve highly selective fishing (significantly reducing bycatch) or precise feeding (reducing
feed pollution); and, in the future, equipment fleets could collaboratively perform specific ecological restoration
tasks (e.g., assisted seeding, invasive species removal) [27]. The ultimate goal is to transition intelligent fishing
equipment from being "extractors" and "disturbers" of the environment to becoming "perceivers," "guardians,"
and "participants" in the urban marine ecosystem, thereby enhancing the resilience and sustainability of the entire
social-ecological system (Figure 2).
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In summary, urban oceanography provides a grand, systematic framework for examining and addressing marine
environmental problems under predominant human influence. It situates the advancement of intelligent fishing
equipment within a broader context of urbanization, emphasizing not only the necessity of technological
innovation but also clarifying its ultimate purpose: serving the sustainable management of this coupled system.
By deeply integrating equipment into the complete chain from sensing to decision-making and action, we can
bridge the gap between urban development and marine conservation, laying a solid technical and scientific
foundation for achieving blue growth.
Urban oceanography provides a vital theoretical foundation for understanding the complex mechanisms of
interaction between human activities and the marine environment. This chapter proposes and systematically
elaborates the core conceptual framework of the "Sensing-Decision-Action" cycle (Figure 3), which illustrates
how intelligent fishing equipment couples with urban oceanography principles to form a dynamic, self-adapting
governance system for the urban ocean.
Traditional marine management models exhibit distinct "open-loop" characteristics: decisions are based on
limited and lagging information, and policy adjustments severely lag behind ecosystem changes. In contrast, the
"Sensing-Decision-Action" framework constructs a closed-loop feedback system informed by Complexity
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Theory and Adaptive Governance. Within this system, intelligent fishing equipment acts as the "sensing organs,"
urban oceanography models serve as the "decision-making brain," and technology applications guided by
sustainable engineering act as the "execution means." These three components are interlinked through continuous
feedback, forming an intelligent governance cycle with self-learning and iterative optimization capabilities as
presented in Figure 3.
The role of intelligent fishing equipment evolves into a key node for constructing a highresolution, real-time
marine monitoring network. Vessels, USVs, and underwater robots equipped with multi-parameter sensors
become mobile observation stations, acquiring key environmental parameters and biological data. This "mobile
sensing" paradigm offers unparalleled spatial coverage, effectively capturing the heterogeneous characteristics
of the urban nearshore environment.
Intelligence: Vast amounts of raw data are assimilated and fused with information from satellite remote sensing
and socio-economic statistics within urban oceanography models. These models simulate ecosystem responses
under different management scenarios, enabling a shift from experiential judgment to evidence-based, predictive
decision-making. This enhances the foresight and scientific rigor of governance strategies.
Scientific decisions are translated into precise operations through intelligent equipment. For instance, trawlers
with AI vision can achieve speciesselective harvesting, while automated feeding vessels can optimize feeding
strategies. The equipment itself becomes a tool for policy enforcement, such as through electronic fence systems.
Crucially, the effects of these actions are immediately captured by the sensing network, forming a vital feedback
loop to the decision-making brain for continuous optimization.
The prominent value of this framework lies in its powerful interdisciplinary integration capacity. It organically
weaves technology, science, and engineering into a functionally synergistic whole, providing a clear path to
overcome the disconnection between intelligent fishing equipment R&D and macro-ecosystem management.
The SDA cycle essentially constructs a digital-era governance system for the urban marine social-ecological
system.
The effective operation of the SDA framework relies on advancements in several key technological domains.
This chapter reviews the core technologies that empower the Sensing, Decision, and Action phases, with a focus
on their integration within the urban ocean context.
A. Environmental Sensing Technology Intelligent fishing equipment serves as mobile monitoring platforms,
deploying integrated sensor networks to collect real-time data on the urban marine environment.
Smart vessels, Unmanned Aerial Vehicles (UAVs), and underwater robots simultaneously conduct operations
and monitor key physical, chemical, and biological parameters (e.g., temperature, salinity, dissolved oxygen,
pollutants, chlorophyll-a).
Internet of Things (IoT) technology provides the "neural network" for real-time data transmission from these
dispersed nodes. Artificial intelligence algorithms, such as using Evolutionary Product Unit Neural Networks to
reconstruct large missing data sets from ocean buoys ensures the quality and integrity of the input data [23].
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This technology layer transforms raw data into actionable knowledge through advanced detection and intelligent
analysis.
AI-assisted sonar imaging, satellite remote sensing (e.g., Synthetic Aperture Radar, optical sensors), and UAV
aerial photography enable accurate identification and monitoring of marine resources and human activities,
supporting fishing ground prediction and vessel monitoring. For example, Synthetic Aperture Radar (SAR) and
optical sensors can provide large-scale, high-resolution sea surface information without being restricted by
weather or day and night, creating conditions for fishing ground prediction and vessel monitoring [23].
Machine learning (e.g., Random Forest, XGBoost) and deep learning models (e.g., CNN, LSTM) analyze multi-
source data for accurate fishery forecasting, stock assessment, and detection of illegal activities. A promising
trend is the fusion of physical oceanographic models with AI, enhancing model generalizability and
interpretability for complex management decisions.
This domain translates intelligent decisions into precise, low-impact interventions, forming closedloop control
systems. Key applications include selective harvesting systems, which use AI visual recognition (e.g., YOLO,
Mask R-CNN algorithms [24], [26]) for real-time species identification to reduce bycatch; automated aquaculture
systems that leverage sensors and reinforcement learning to optimize feeding, cutting costs and pollution;
ecological restoration units like underwater robots for tasks such as coral planting; and technologies for
enforcement and traceability, including electronic fence systems and blockchain, to enhance compliance and
transparency.
These technological pillars are synergistically integrated through an architecture (Figure 4). Horizontal fusion
combines sensing, analysis, and execution data for comprehensive decision-making. Vertical fusion distributes
tasks across cloud (largescale data and model training), edge (localized realtime decisions), and end devices (data
collection and execution). Emerging paradigms like Digital Twin technology, which creates precise virtual
replicas of the physical marine environment, offer powerful tools for scenario simulation and system-wide
optimization, signaling a shift from isolated innovations towards integrated system solutions.
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This multi-level technological fusion creates systemic efficacy unattainable by any single technology. For
example, by integrating and analyzing real-time environmental data, resource distribution information, and
equipment operational status, the system can dynamically optimize the operational efficiency of the entire fishery
production chain, while coordinating resource use and ecological protection across larger spatial scales and
longer time dimensions. Currently, Digital Twin technology is emerging as a new paradigm for achieving this
kind of system integration. By constructing precise mappings between the physical world and virtual space, it
provides a powerful tool for scenario simulation, strategy testing, and impact assessment in urban ocean
management. The development and maturation of these technologies signify that intelligent fishing equipment
is transitioning from isolated technological innovations towards system integration, offering unprecedented
technological opportunities for the transformation and upgrading of urban ocean governance models.
Following the establishment of the "Sensing Decision-Action" theoretical framework and the clarification of its
technological underpinnings, translating this innovative paradigm into viable governance practices emerges as a
critical challenge. The operationalization of the SDA framework across different urban ocean governance layers
is synthesized in Table Ⅰ, which maps the interconnections between sensing tools, decision models, and action
outcomes. This chapter systematically explores the implementation pathways illustrated by this synthesis,
focusing on three dimensions: urban marine spatial planning, data driven decision-making mechanisms, and
multistakeholder collaboration.
TABLE Ⅰ Operationalization of the Sensing-Decision Action Framework
SDA Phase
Governance
Function
Key Technologies
Illustrative Application
(Zhoushan Case)
Sensing
Data
Acquisition
Vessel/US
V sensors, IoT, Satellite RS,
eDNA
Detecting hypoxia zones & fish
spawning.
Decision
Analysis &
Strategy
Data fusion, Digital Twins, Predictive
models, DSS
Recommending temporary
spatial closures.
Action
Implementation &
Enforcement
AI-selective gear, Robotic restoration,
Blockchain, Geofencing
Automated vessel rerouting &
compliant fishing.
Feedback
Loop
Learning &
Adaptation
Continuous data flow from Sensing to
Decision models
System learning for future
optimization
At the level of urban marine spatial planning, the data revolution brought by intelligent fishing equipment is
profoundly transforming traditional planning methodologies. The continuous acquisition of high-resolution
environmental data through equipment sensor networks, combined with resource distribution information from
AI-assisted sonar and remote sensing technologies, enables a shift from static zoning to dynamic and adaptive
spatial management. To illustrate the feasibility of the Sensing-Decision-Action (SDA) framework, a case study
of dynamic spatial management in the Zhoushan Archipelago, a representative urban coastal region in China, is
presented.
The process began with a comprehensive sensing phase. A network of smart fishing vessels and unmanned
surface vehicles (USVs), operating in the archipelago's waters, continuously collects real-time data on water
temperature, salinity, dissolved oxygen, and chlorophyll-a levels. Concurrently, AI-powered analysis of satellite
remote sensing imagery and vessel-based acoustic data monitors the distribution and spawning aggregation of
key commercial fish species (e.g., hairtail, small yellow croaker) as well as the occurrence of harmful algal
blooms (HABs).
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The multi-source data stream acquired through sensing was subsequently integrated and analyzed in the decision
phase by an urban oceanography decision-support system (DSS). The model identifies a conflict: a traditional,
statically designated fishing ground overlaps with a temporally emerging hypoxia zone and a newly identified
spawning aggregation of a non-target species. The DSS simulates various management scenarios and
recommends the dynamic adjustment of functional zone boundaries. It proposes the temporary closure of the
specific grid cell to bottom trawling to protect the spawning aggregation and avoid the hypoxic zone, while
suggesting an alternative, productive area for fishing activities.
Following the decision, the prescribed management rules can be automatically implemented in the action phase.
These rules, encoded as smart contracts within a blockchain-based platform, could be updated. Vessel monitoring
systems (VMS) and electronic fence systems on board the intelligent fishing fleet would receive the new
geofencing coordinates. A trawler approaching the temporarily restricted zone could receive an automated alert,
and its route optimization system would then recalculate a path to the suggested alternative area, thereby ensuring
compliance. The vessel's own sensors would continue to monitor environmental conditions in the new area,
feeding data back into the system.
This dynamic spatial management approach, powered by the SDA closed loop, demonstrates the potential to not
only enhance the scientific rigor of planning but also significantly improve its adaptability and resilience. It
shows promise in effectively coordinating diverse functional demands—such as aquaculture, fishing,
conservation, and shipping—minimizing spatial use conflicts and enhancing the comprehensive utilization
efficiency of urban marine space.
Intelligent fishing equipment enables a "monitoring-assessment-decision" closed-loop management model,
which relies on three core pillars: first, a cross-sectoral data governance platform with unified standards to break
down silos and enable secure data sharing; second, management-oriented Decision Support Systems (DSS) that
translate complex models into intuitive scenarios for decisionmakers; and third, automated enforcement systems
using blockchain-based smart contracts to ensure efficient policy implementation. The example from the
Zhoushan Archipelago shows this synergy establishes a traceable governance model.
Intelligent equipment provides the technical linkage for polycentric governance. For instance, governments can
provide public data platforms, research institutions utilize long-term data for deeper insights, and fishers and the
public can access the network via mobile terminals as both data providers and supervisors. This technology-
enabled model enhances inclusivity and transparency, while its longterm stability requires incentive-compatible
mechanisms to ensure all parties’ benefit.
The preceding analysis demonstrates that the deep integration of intelligent fishing equipment and urban
oceanography opens new horizons for a vibrant cross-disciplinary research field. However, the transition from a
theoretical framework to effective practice faces multifaceted challenges that demand a research agenda
extending far beyond technological innovation. This chapter systematically proposes key future research
directions, encompassing the critical yet often overlooked dimensions of data governance, socio-economic
equity, and policy integration, to provide sustained academic support for urban ocean sustainability.
It must be specifically pointed out that the transition from framework to practice faces numerous challenges.
Technically, the compatibility of different equipment systems and the standardization of data protocols require
further refinement [24]. The lifeblood of the SDA framework is data. Future research must urgently address the
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governance of this data to ensure its responsible and effective use.
Research is needed to develop balanced data governance models that reconcile data openness for public value
with privacy protection and commercial interests. This includes exploring standardized data protocols for
different sensor systems and designing secure, incentive-compatible data-sharing platforms that break down silos
between government agencies (fisheries, environment, maritime affairs) and private actors (fishers, technology
providers).
Technical research should focus on enhancing the compatibility of different equipment systems and data formats.
Promoting the alignment of Chinese technical standards with international ones (e.g., those emerging from FAO
and IEEE) is crucial for scaling solutions and fostering global collaboration.
The agenda must include critical studies on data ownership, algorithmic bias in AI-driven decision models, and
equitable access to the benefits generated by intelligent technologies. Research should ensure that these systems
do not disproportionately marginalize small-scale fishers or communities with limited digital literacy.
The technological transformation will inevitably trigger significant socio-economic shifts. Proactive research is
essential to guide a just and inclusive transition.
In-depth socio-economic studies are needed to analyze the impact pathways of intelligent transformation on
traditional fishing livelihoods. This should be followed by action research to design targeted skills training
programs, social safety nets, and alternative livelihood options to facilitate a smooth labor force transition and
foster community resilience.
Research should explore the feasibility of new business models, such as equipment-sharing platforms, data-as-
a-service subscriptions, and catch certification schemes, to lower the adoption threshold for small and medium-
sized enterprises (SMEs). Cost-benefit analyses and innovative financing mechanisms (e.g., public-private
partnerships, green loans) are needed to address the high initial investment costs and prove the long-term
economic viability of the SDA approach.
Studies analyzing the social acceptance of intelligent fishing technologies among different stakeholder groups
(fishers, policymakers, consumers, NGOs) are vital. Research should design and test effective science
communication and participatory governance schemes to cultivate social license and integrate local knowledge
into the management system.
Technological innovation must be matched by parallel innovation in governance structures and policy
instruments.
Future work should design and pilot policy instruments that link environmental and operational performance
metrics (e.g., bycatch rate, carbon emission intensity, compliance with spatial closures) monitored by intelligent
equipment with economic incentives like preferential fishing quotas, tax breaks, or government subsidies. This
creates a direct feedback loop between sustainable practices and economic reward.
Research is required to develop adaptive legal frameworks that can accommodate and legitimize automated
enforcement mechanisms, such as blockchain-based smart contracts and VMS-driven geofencing, ensuring they
are implemented fairly and with appropriate oversight.
Institutional analysis should focus on designing and evaluating new organizational structures (e.g., inter-agency
task forces, data collaboratives) that can overcome traditional departmental silos and enable the integrated, cross-
sectoral decision-making demanded by the urban oceanography perspective.
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Addressing these complex challenges necessitates a genuine cross-disciplinary fusion paradigm that moves
beyond mere collaboration. There is a need to establish integrated research platforms that blend marine science,
information engineering, social sciences, ethics, and management science. Specifically, the formation of deeply
integrated research teams should be encouraged, and novel methodologies that combine technical prototyping
with participatory action research and policy analysis should be developed. Cultivating talents with multi-
disciplinary literacy is paramount. Through such comprehensive collaboration, research at the intersection of
intelligent fishing equipment and urban oceanography is bound to make significant contributions to building a
modern governance system for harmonious coexistence between humans and the ocean.
This paper has systematically demonstrated the necessity and innovative pathways for repositioning intelligent
fishing equipment within the theoretical framework of urban oceanography. The core achievement of this
research lies in proposing the "Sensing-Decision-Action" (SDA) conceptual framework. This model represents
a significant advancement beyond traditional intelligent fisheries frameworks, which primarily focus on
optimizing isolated technological parameters (e.g., gear efficiency, vessel fuel economy). In contrast, the SDA
framework systemically elevates intelligent fishing equipment from mere production tools to indispensable core
infrastructure within a sustainable urban ocean governance system. By assigning them the multiple,
interconnected roles of "sensing organs," "decision-support engines," and "precision execution means," and
integrating them into a closed-loop social-ecological system (as synthesized in Table Ⅰ), this framework provides
the theoretical basis and technical implementation path for achieving refined, adaptive, and learning-oriented
governance of the highly complex and dynamic urban marine environment.
This research profoundly reveals that the successful implementation of the SDA framework critically depends
on deep cross-disciplinary integration and collaborative innovation across marine science, information
engineering, mechanical manufacturing, environmental policy, and socioeconomics. It serves not only as a
platform for technological integration but also as a hub for knowledge fusion, thereby bridging the critical gap
between technological development and ecosystemlevel management practice.
Promoting the application and development of this framework holds significant importance for China's pursuit
of its "Marine Power" strategy. It drives the transformation and upgrading of marine industries through
technological innovation, safeguards marine ecological security through intelligent governance, and enhances
comprehensive marine management capabilities through data
integration. It is thus a crucial lever supporting highquality development of the ocean economy and achieving
harmonious coexistence between humanity and nature.
Looking forward, the new paradigm of urban ocean governance led by the "Sensing-DecisionAction" framework
heralds a smarter, more sustainable blue future. As technologies like the Internet of Things, Artificial Intelligence,
and Digital Twins continue to mature and become more costeffective, intelligent fishing equipment will weave
a more extensive and responsive marine sensing network. Urban ocean management will transition from being
reactive to proactive and predictive, from experience-based to model-driven, and from static control to dynamic
optimization. We can anticipate a new paradigm of positive interaction between human activities and marine
ecosystem health, where intelligent equipment acts as a key link, assisting human society in effectively assuming
the responsibility of ocean stewardship while enjoying its bounty, ultimately guiding us collectively towards a
sustainable marine future.
This work is supported by the Provincial Discipline Construction University-level Interdisciplinary Project:
Interdisciplinary Research on Urban Oceanography and Fishing Equipment (Grant No. 13024060221).
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