Hydrographic offices and maritime authorities are facing unprecedented challenges: growing data volumes, tighter timelines and increasing demands for accuracy and interoperability. Traditional workflows, while reliable, often struggle to keep pace with the complexity of modern hydrospatial environments. By integrating geospatial AI in a GIS, maritime authorities can advance their mission in supporting navigation, promoting resilience and advancing the blue economy.
Today, artificial intelligence (AI) and machine learning are emerging as transformative tools, driving smarter workflows that automate repetitive tasks, enhance quality control and unlock new applications. For example, AI assistants now play a role in help systems, documentation and code generation, using natural language to ask questions or guide software tasks.
Integrating geospatial AI with these technologies in a geographic information system (GIS) allows maritime authorities to move beyond chart production and toward true hydrospatial agency roles that support navigation, promote ocean and coastal resilience, and advance the blue economy.
The case for AI in hydrography
Hydrographic data acquisition is evolving, with multibeam, Lidar and satellite imagery generating terabytes of data during a single survey. Manually processing this information is time-consuming and susceptible to human error. Beyond automating bathymetric data cleaning, modern workflows require analysis and identification of features and trends within data. Geospatial AI addresses these challenges by enabling automated processes and predictive capabilities throughout the workflow.
Machine learning models can identify features such as wrecks, rocks and seabed anomalies quickly and accurately. Quality control processes – traditionally requiring extensive human review – are now streamlined through AI-driven anomaly detection and automated compliance checks. These technological advancements not only reduce operational costs but also accelerate the delivery of Electronic Navigational Charts (ENCs) and bathymetric surfaces. One excellent example of early automation capabilities that massively improve efficiency is Esri’s Custom Chart Builder. Introduced in 2016, it fully automates paper chart production in compliance with international standards. Hydrographic offices are using Custom Chart Builder to eliminate the traditional manual process to produce a paper chart, replacing a process that used to take months with an automated process that delivers results within minutes.
Smart workflows with GIS
Esri’s ArcGIS Maritime platform integrates geospatial AI into end-to-end workflows, ensuring hydrographic offices adhere to international standards while innovating beyond traditional charting. Smart workflows powered by AI enable:
- Automated feature detection: Deep learning models identify objects from bathymetric grids and imagery, reducing manual digitization efforts.
- Quality control at scale: AI-driven validation ensures compliance with International Hydrographic Organization (IHO) S-100 standards and detects inconsistencies across datasets.
- Data integration: GIS connects multibeam, Lidar and satellite data in a unified environment, enabling seamless analysis and visualization.
- Real-time insights: With AI-enabled GIS, organizations can monitor vessel traffic and environmental conditions in near real time, supporting operational safety and efficiency.
These capabilities transform hydrographic offices into agile, data-driven organizations capable of supporting marine spatial planning, port operations and environmental monitoring.
Real-world applications
AI-enabled workflows deliver tangible benefits across the maritime domain, including:
- Disaster response: Automated wreck detection accelerates postevent assessments, ensuring navigational safety after hurricanes or tsunamis.
- Predictive dredging: Machine learning models forecast sediment movement, optimizing dredging schedules and reducing costs.
- Coastal resilience: AI supports vulnerability assessments by analysing historical and real-time data, informing strategies for climate adaptation.
- Blue economy initiatives: Hydrospatial data enriched by AI enables sustainable fisheries management and offshore energy planning.
These applications demonstrate that AI is not just a technological upgrade but a strategic enabler for maritime authorities seeking to expand their role in ocean governance.
New horizons
Geospatial AI in ArcGIS, when combined with ontology-based semantic databases, enables geospatial systems that are not only data-driven but also knowledge-driven. An ontology-based semantic database stores data together with an explicit, machine-readable model of domain concepts, relationships and rules (an ontology). This allows data to be interpreted, linked and reasoned about, based on meaning rather than just structure.
As mentioned above, in ArcGIS, geospatial AI is used to automatically extract, classify and enrich spatial data from imagery, sensor feeds such as bathymetry, time series and vector datasets. Machine learning and deep learning models detect features, predict patterns and identify anomalies. When these outputs are linked to an ontology-based semantic database – often implemented as a knowledge graph – the AI results are semantically grounded: detected features are mapped to well-defined concepts, attributes and relationships, rather than remaining isolated predictions.
ArcGIS Knowledge plays a key role by representing entities and relationships as a graph aligned with ontology concepts. The semantic database defines domain meaning – such as hydrographic features, infrastructure assets, environmental processes or S-100 concepts – while geospatial AI models populate, update and validate this semantic structure at scale. AI can also infer missing relationships, suggest classifications and support probabilistic reasoning that complements rule-based ontology logic.
This combination enables advanced capabilities such as natural language queries translated into spatial and semantic queries, cross-domain data integration, explainable AI results tied to explicit definitions, and decision support systems that combine prediction with contextual understanding. In marine, environmental and urban applications, this fusion supports safer navigation, smarter planning and interoperable geospatial ecosystems.
Outlook
The next frontier for hydrography lies in digital twins and autonomous systems, which will generate exponentially larger datasets and demand new processes and advanced tools, such as analytics and automation, to manage, interpret and leverage this data efficiently. AI will underpin these innovations, providing predictive analytics for ports, shipping lanes and coastal ecosystems.
The maritime industry stands at a crossroads. By adopting AI-powered workflows through platforms like ArcGIS Maritime, hydrographic offices can accelerate production, improve quality and unlock new capabilities. Human-AI collaboration will remain essential, with experts guiding models and interpreting results to ensure accuracy and trustworthiness. As hydrographic offices embrace these technologies, they will evolve into hydrospatial agencies that operate more efficiently and are capable of delivering insights that drive safety, sustainability and economic growth. It’s the future of hydrography: smart, automated and integrated.