Machine Learning Models from Regression to Deep Learning Neural Networks for Assessment, Prediction, Mitigation, and Control of Geospatial, Socioeconomic, and Environmental Impacts of Climate Change

Dear Colleagues,

Thanks to hardware advances, incredible computational resources, and advancements in data acquisition in the past two decades, Machine learning (ML) models can now be applied to virtually all sorts of problems and are finding applications across multiple disciplines. Today, it is feasible and practical to apply ML models to tackle complicated multidisciplinary problems.

As a global society, we have constructed and organized our daily lives based on climate conditions. We have been continuously adapting to new ranges of climate conditions. We have become accustomed to new climate conditions and considered them to be the new normal, while we have been sensitive to extremes that have fallen outside of the normal ranges. Extreme climate conditions such as large storms, longer drought periods, and heat waves have been occurring more frequently and have become more intense. Climate change is impacting our global society through environmental, economic, social, and cultural changes, and extreme climate conditions are affecting water supplies, energy, food, public health, natural resources, and infrastructures.

As it relates to global climate change and extreme climate conditions, including drought, fire, sea-level rise, and coastal storms, this is the most crucial time to employ and implement ML models for assessment and prediction of geospatial, environmental, and socioeconomic impacts of climate change. This is an essential step to make sound decisions and adopt appropriate policies to mitigate and control climate change impacts. This Special Issue of Remote Sensing addresses the broad area of climate change and its impacts on our global society. We seek high-quality articles focused on ML techniques, including but not limited to regression models, clustering algorithms, and classification methods for processing remotely sensed data for evaluation, prediction, mitigation, and control of climate change impacts.

Dr. Nezamoddin N Kachouie
Guest Editor

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.

• Machine learning models
• Regression models
• Deep learning neural networks
• Climate change impact
• Socioeconomic
• Environmental
• Public health
• Cultural