Topic Editors

Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Prof. Dr. Yu Li
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Faculty of Geography, University of Belgrade, Studentski Trg 3/3, 11000 Belgrade, Serbia

Advances in Sensor Data Fusion and AI for Environmental Monitoring

Abstract submission deadline
30 July 2026
Manuscript submission deadline
30 September 2026
Viewed by
9

Topic Information

Dear Colleagues,

Environmental monitoring increasingly demands high-resolution, real-time, and reliable information to guide sustainability, disaster response, and ecosystem management. Advances in sensor technologies—including satellites, airborne platforms, in situ stations, and Internet of Things (IoT) devices—have enabled the collection of vast amounts of heterogeneous data (e.g., spectral, structural, chemical, and meteorological observations). However, transforming these multimodal streams into actionable insights remains challenging, necessitating effective data fusion strategies and robust artificial intelligence frameworks. This Topic emphasizes research at the intersection of sensor data fusion and AI-driven analytics, aiming to highlight innovations that integrate multi-source data for accurate environmental assessment and predictive modeling.

Contributions are encouraged in areas such as:

(1) Novel fusion algorithms for heterogeneous sensor integration, especially those improving spatial and temporal resolution;

(2) Deep learning models tailored to fused data for applications such as land-cover change, air and water quality, forest health, and disaster prediction;

(3) Scalable and efficient architectures—e.g., edge-to-cloud systems or federated learning—for near‑real‑time monitoring;

(4) Case studies demonstrating improved decision-making in forestry, agriculture, urban planning, or conservation. Ultimately, this Topic seeks to showcase multidisciplinary approaches that leverage sensor fusion and AI to advance environmental science and inform sustainable resource management.

Dr. Zhenyu Yu
Prof. Dr. Mohd. Yamani Idna Idris
Prof. Dr. Yu Li
Dr. Aleksandar Dj Valjarević
Topic Editors

Keywords

  • artificial intelligence (ai)
  • sensor data fusion
  • remote sensing
  • forest resource assessment
  • environmental monitoring
  • smart agriculture
  • machine learning
  • multisource data integration
  • ecological modeling
  • sustainable management

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Geosciences
geosciences
2.1 5.1 2011 23.4 Days CHF 1800 Submit
ISPRS International Journal of Geo-Information
ijgi
2.8 7.2 2012 34.2 Days CHF 1900 Submit
Remote Sensing
remotesensing
4.1 8.6 2009 24.9 Days CHF 2700 Submit
Sensors
sensors
3.5 8.2 2001 19.7 Days CHF 2600 Submit
Data
data
2.0 5.0 2016 25.2 Days CHF 1600 Submit

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