Dear Colleagues,

Remotely Sensed (RS) images, such as spectral satellite images and the Synthesis Aperture Radar (SAR), viewing large parts of the earth, play an important role in addressing many different scientific and socio-economic problems around the world. The research on remotely sensed data based on different computational strategies, Machine Learning (ML) and Artificial Intelligence (AI) techniques range from image enhancement, scene classification and segmentation to change detection, time-series analysis for detection and prediction purposes and multimodal data fusion for an improved prediction. Such analyses are used for numerous applications. Examples include, but are not limited to, monitoring the challenges and changes in the environment, such as forests, volcanoes, rivers and coastal areas, roads and urban areas, an estimation of the boundaries and level of disasters in earthquakes and natural fires, managing farms and agricultural areas and all challenges induced by carbon emissions and climate change. On the other hand, the fast-growing number of advanced ML, AI and image analysis algorithms, especially Deep Learning (DL) strategies and their deployment to the remote sensing domain, have enabled researchers to tackle computationally demanding problems in remote sensing, such as the analysis of the time-series of remotely sensed data or hyperspectral satellite imagery and real-time/near real-time data processing for applications such as the tracking and monitoring of natural disasters or human-induced hazards. That is supported by the advances in high performance computing, allowing the processing systems to perform parallel computations based on advanced ML and AI algorithms on high volumes of remotely sensed data and reduce the running time.

Considering all of the above-mentioned applications and opportunities, this Special Issue invites researchers from both academia and the industry to contribute their novel research findings for solving the wide range of existing challenges, addressing new application scenarios and identifying new opportunities in the remote sensing domain, by developing new AI and ML methods or demonstrating the application of the existing methods.

Topics may cover anything from classical image processing and the computer vision problems related to the remote sensing domain, to more recent and advanced topics such as satellite image time-series analysis and multimodal data fusion for joint decision making (e.g., multispectral, hyperspectral and SAR images). Review papers addressing the novel challenges in remote sensing will also be considered.

Dr. Sara Sharifzadeh
Dr. Priti Bansal
Dr. Daniel M. Simms
Prof. Dr. Vasile Palade
Guest Editors

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.

• land cover and land use detection and monitoring
• time-series analysis of RS data for detection/prediction
• real-time/near real-time RS data analysis
• multimodal data fusion of RS data for decision-making
• RS image enhancement for undesired effects (cloud, noise, etc.)
• data augmentation for improved decision-making
• object tracking based on RS data
• optimization techniques for improved modeling in RS data analysis domain