Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing II
Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 28496
2. McGregor Coxall Australia Pty Ltd., Sydney, NSW, Australia
Interests: machine learning; geospatial 3D analysis; geospatial database querying; web GIS; airborne/spaceborne image processing; feature extraction; time-series analysis in forecasting modelling and domain adaptation in various environmental applications
Special Issues, Collections and Topics in MDPI journals
Following the success of the previous Special Issue "Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing", a new one has been activated.
Artificial intelligence (AI) and machine learning (ML) techniques have been a principal element of image processing and spatial analysis in numerous applications for a decade. AI enables us to determine the real function of imagery data and process it with a well-fit algorithm to model a structural framework in terms of classification, regression, and clustering, and to model the spatial correlation. Deep neural networks, usually known as deep learning, are one of the robust methods of ML that can engage numerous layers of data-driven algorithms to perform a wide range of applications including pattern recognition, feature detection, trend prediction, instance segmentation, semantic segmentation, and image classification in the form of neural networks.
Conventional structured remotely sensed data need to be labelled manually when it comes to training model, which is a subjective user-centric, untransferable, tedious approach. Therefore, it is important to eliminate these uncertainties by establishing a reproducible and reliable approach, which can be referred to as “machine vision” (MV). MV attempts to leverage the current AI technology in a novel way in order to provide an automatic inspection workflow from image acquisition from the sensor to digital image pre-processing, training and testing techniques, validation, and knowledge extraction. It covers software products and hardware architects such as CPU, GPU/FPGA combination, parallel implementation, and computer vision to minimize computation time while maximizing the reproducible accuracy.
In this Special Issue, we welcome the submission of scientific manuscripts proposing a framework to leverage MV with optimized AI techniques and geospatial information systems to automate the processing of remotely sensed imagery from, for example, lidar, radar, SAR, and multispectral sensors with higher precision for multiple spatial applications including but not limited to urbanism, land-use modelling, environment, weather and climate, energy sector, natural resources, landscape, geo-hazards, etc
Dr. Hossein M. Rizeei
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- artificial intelligence (AI)
- machine vision (MV)
- machine learning (ML)
- geospatial information systems (GIS)
- spatial framework
- deep learning (DL)