AI-Enhanced Sensor Data Integration and Processing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 15 October 2026 | Viewed by 17
Special Issue Editor
Special Issue Information
Dear Colleagues,
The rapid proliferation of high-resolution sensor observations presents unprecedented opportunities for environmental and oceanic sciences, while simultaneously imposing greater computational demands on numerical modeling. This Special Issue focuses on the critical challenge of efficiently assimilating diverse sensor data into dynamical models under acceptable computational costs. We invite contributions that explore AI-driven approaches to enhance data assimilation processes, addressing fundamental bottlenecks in computational efficiency and the mismatch between model physics and observational variables. We particularly focus on addressing two fundamental challenges:
- Computational bottlenecks
Sensor data assimilation requires multiple runs of PDE solvers in each optimization iteration. The high flux of sensor observations, e.g., high-resolution satellite data, demands numerical 1 models to generate comparable volumes of state variables for correspondence, making forward computation the primary cost source.
- Observation-to-state variable mapping
Sensor measurements Z(x, t) do not directly correspond to the state variables U(x, t) solved by numerical models. Creating effective linkages that allow observational data to constrain and inform the physical system dynamics requires sophisticated mappings that current empirical approaches handle inadequately, limiting assimilation effectiveness.
We invite researchers to contribute innovative solutions that leverage artificial intelligence to bridge these gaps and advance state-of-the-art technologies in environmental data assimilation.
Dr. Jun Li
Guest Editor
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Keywords
- machine learning surrogate models for accelerating forward operators
- neural network-based observation operators for sensor data
- deep learning approaches for variational data assimilation
- AI-driven ensemble data assimilation techniques
- hybrid physics-AI models for environmental prediction
- multi-sensor fusion using AI techniques
- real-time processing of high-resolution sensor streams
- quality control and bias correction of sensor observations
- uncertainty quantification in sensor data assimilation
- adaptive sensor placement and observation strategies
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