Data Fusion and Artificial Intelligence Applications in Remote Sensing
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 17217
Special Issue Editors
Interests: data fusion and AI; UAV-based remote sensing; environmental remote sensing
Interests: geospatial big data; dynamic monitoring; high-speed videogrammetry
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advancements in sensors and high-performance computing platforms have resulted in the development and implementation of numerous state-of-the-art deep/machine learning approaches for conducting multisensor collaborative remote sensing (RS) applications. Specifically, this includes the emergence of new sensors and more powerful versions of traditional sensors, the increasing availability of additional sophisticated space, aerial, and ground platforms, the automation of data processing and the popularization of geospatial artificial intelligence (GeoAI). However, it is necessary to mention that the users’ demands and expectations with respect to the size of the observed area, temporal and spatial resolution, accuracy, speed of operation, problem-solving capability and new application developments using multisource RS technology are still increasing. Therefore, it is necessary to deeply integrate data fusion and GeoAI according to the characteristics of RS data from different sources and the specific needs of different applications.
This Special Issue focuses on advancements and innovative methods and solutions of data fusion and geospatial artificial intelligence (GeoAI) in remote sensing (RS). To highlight new solutions of data fusion and GeoAI algorithms for RS applications and problems, manuscript submissions are encouraged from a broad range of related topics, which may include but are not limited to the list below:
- Fundamental theory for data fusion and GeoAI;
- Deep/machine learning method algorithms;
- Registration of multisensor and multiresolution imagery;
- Synergies between satellite, UAV and ground-based remotely sensed data;
- Multisensor, multiresolution and spatiotemporal image fusion;
- Application-oriented data fusion and GeoAI for classification, change detection, agriculture and crop mapping, etc.;
Prof. Dr. Lei Deng
Prof. Dr. Xianglei Liu
Guest Editors
Manuscript Submission Information
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