XOCEAN builds AI on AWS to automate seabed survey interpretation
XOCEANAWSXOCEAN, an ocean data company operating a fleet of uncrewed survey vessels, streams seabed survey data directly to AWS and is building AI, deployed on AWS infrastructure, to automate interpretation of seabed imagery such as identifying and measuring boulders that geophysicists currently review manually. The goal is real-time pass/fail assessments during surveys, which the company expects could cut project schedules by ten days to two weeks per site.
Overview
XOCEAN, an ocean data company operating a fleet of uncrewed survey vessels, streams seabed survey data directly to AWS and is building AI, deployed on AWS infrastructure, to automate interpretation of seabed imagery such as identifying and measuring boulders that geophysicists currently review manually. The goal is real-time pass/fail assessments during surveys, which the company expects could cut project schedules by ten days to two weeks per site.
This entry has 13 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
The seabed at survey sites is scattered with hundreds of thousands of boulders that must be viewed and measured; this is currently done manually by a human and takes a lot of time.
The solution
XOCEAN is working to automate large parts of seabed data interpretation using AI built and deployed on AWS infrastructure, with the goal of enabling real-time pass/fail assessments during surveys to accelerate delivery timelines.
Reported business value
This approach could reduce project schedules by ten days to two weeks per site, while allowing experienced geophysicists to focus on higher-value analysis and interpretation.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other energy entries in the register.
Building a clean energy future with natural language analytics
Williams, a large-scale natural gas infrastructure operator, deployed Databricks AI/BI Genie to give commercial, regulatory, accounting and technical staff natural-language, self-serve access to analytics. The team flattened 27 disparate tables into SQL models that Genie Spaces reason over, encoding internal acronyms and business logic into Genie's instructions, powered by Databricks Unity Catalog. A data request that previously took an analyst five days now completes in seconds with validated accuracy, and the weekly backlog of data requests dropped from up to ten to one or two, freeing analysts for predictive modeling and enterprise projects.
WilliamsDatabricks AI/BI Genie · Databricks Unity Catalog
Plenitude builds machine learning models on Databricks to forecast energy demand and renewable production
Eni-owned energy company Plenitude, which serves 10 million households and businesses across Europe, uses statistical models and machine learning on the Databricks Data + AI Platform to forecast customer energy consumption at hourly and daily granularity, forecast wind and solar generation from its renewable asset portfolio, and run customer segmentation and propensity models across 60 implemented use cases.
PlenitudeDatabricks Data + AI Platform · Delta Lake · Unity Catalog +2Shell delivers innovative energy solutions with data and AI on Databricks
Shell built its Shell.ai platform on Databricks to unify data, analytics, and AI workloads for its 600-member mathematics, computational and data science team, running over 100 AI applications including an inventory prediction model running 10,000+ simulations across spare parts and facilities, and an AI-powered recommendation engine for its loyalty program.
Using data to power-fuel the transition to a carbon-neutral world
Helen, Helsinki's energy utility, built a centralized data and AI platform on Databricks to power forecasting and optimization models for its district heating system serving about 90% of Helsinki's population, processing real-time streaming sensor and IoT data to optimize distributed energy resources and EV charging placement as part of a plan to cut carbon emissions over 80% by decommissioning coal plants.
Helen· FinlandDatabricks Data + AI Platform · Delta Lake · Spark Declarative Pipelines +3Was this helpful?
Your feedback helps us improve our use case database