Machine Learning and Automation in Remote Sensing Applied in Hydrological Processes
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".
Deadline for manuscript submissions: 15 January 2025 | Viewed by 1407
Special Issue Editors
Interests: automation; flood risk; earth science; GIS; land use; hydrology; UAV; drone; remote sensing; structure from motion
Special Issues, Collections and Topics in MDPI journals
Interests: biogeography; hydrology; GIS; remote sensing; geo-informatics; phytogeography; hydrological processes; environmental studies
Special Issues, Collections and Topics in MDPI journals
Interests: flash flood modeling; risk modeling; natural hazards; hydrological modeling and forecasting
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As a result of the constantly growing computational power that researchers have access to, and the constant development of AI tools and techniques, there is an increasing demand to automate and develop machine learning tools to study geo-spatial phenomena in a better, faster manner. By including more data, parameters, and different types of remote sensing imagery, studies can be more representative and results can be more easily compared and brought into a “bigger picture” in order to more appropriately provide an assessment of the impact of different hydrological processes and phenomena, such as droughts, flash floods, water balance, soil moisture, evapotranspiration, infiltration, coastal erosion and other water-related topics.
Considering the fact that the changes in hydrological processes and phenomena are more frequent, and manifest significantly faster, in the context of climate change, automated tools and analyses can significantly aid in the rapid decision making prior to (or during) such cases, or even for disaster management and impact mitigation after extreme hydrological events.
The aim of this Special Issue is to provide state-of-the-art knowledge in the field of remote sensing for hydrological processes through the means of machine learning, neural networks, deep learning, artificial intelligence, automation techniques, etc., and promote new approaches and techniques in the field.
This Special Issue addresses (but is not limited to) the following topics:
- Machine learning for hydrological processes;
- Neural networks applied in water-related topics;
- Methodological studies;
- Remote sensing applied in hydrology;
- Automation techniques;
- Tools developed in GIS software (such as ArcGIS, QGIS, Snap, etc.);
- Google Earth Engine;
- Drought analyses;
- Flood risk analyses;
- Automated and semi-automated classifications;
- Artificial intelligence in water studies;
- Water budget analyses;
- Deep learning applications in hydrology;
- Morphometric studies;
- WebGIS platforms for online automation, etc.
Dr. Andrei Enea
Dr. Cristian Constantin Stoleriu
Dr. Marina Iosub
Dr. Fiachra O’Loughlin
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.
Keywords
- automation
- artificial intelligence
- flood risk automation
- GIS tools
- Google Earth Engine
- hydrological processes
- machine learning
- neural networks
- remote sensing automation
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
