Quantitative Geomorphology in the Artificial intelligence Era

Part I. Foundational Quantitative Geomorphology: Introduction, theory and advances in quantitative geomorphology <br>1. Surface morphology and related Earth-surface processes<br>2. The integration of multiple data to understand the evolution of the landscape through large time scales (geological time) and the adaptation of species (living beings and plants) to such changes<br>3. Math for geomorphologists<br>4. Quantitative Geomorphology: mechanics and chemistry landscape<br>5. Digital Terrain Analysis: Principles and Applications<br>6. Quantitative Geomorphometry: Concepts, Software, Applications<br>7. Understanding landscape evolution and interactions with the environment through quantitative methods<br>8. Understanding of how processes are correlated and what the data can tell us about the correlations and feedback processes<br>9. Why and how to quantify processes<br>10. AI and big data in quantitative geomorphology<br>11. Theoretical, experimental, and quantitative geomorphology<br><br>Part-II - The application of quantitative techniques to hot topics in geomorphology<br>12. Past, present, and future environmental changes<br>13. Climate, tectonics, and regional structure, interactions between tectonic and surface processes<br>14. Anthropogenic geomorphology<br>15. Geomorphic hazards or Environmental multi-hazard<br>16. Geodiversity, bio geomorphology, predict species distribution, changes in biodiversity, and their adaptation to climate<br>17. Tectonics and/or anthropogenic processes<br>18. Effect of extreme methodological events or climate change on geomorphological processes and hazards<br>19. The role of quantitative Geomorphology in the development of urban and rural settlements<br>20. Landform classification<br>21. Scale, scaling laws and fractal applications to quantitative geomorphology<br>22. Use, advantages and limitations of big data and advanced technologies in quantitative geomorphology research<br><br>Part-III - Advanced Quantitative Geomorphology<br>23. Geomorphological indices<br>24. Landscape evolution models (LEM)<br>25. Quantitative geomorphology modelling, mapping, and its spatial-temporal variability 26. Applications to quantitative geomorphology to Risk management<br>27. Geophysical detection of surface and under surface landforms using AL ERA<br>28. Sensibility of the data quality in quantitative geomorphology<br>29. Impacts of climate change on geomorphological processes and hazards (e.g., UAV photogrammetry, TLS, ALS, etc.)<br>30. Application of high and very-high spatial and temporal resolution satellite multispectral and stereo imagery in geomorphic research (e.g., Worldview. Geoeye, Dove, etc.)<br>31. Survey of the degradability in the desert area using AL ERA<br>32. Quantitative analysis of watershed geomorphology using AL ERA<br><br>Part-IV –Tools in advanced quantitative geomorphology<br>33. GIS, remote sensing, and spatial modelling methods and applications, sedimentary records, and dating.<br>34. Multi-criteria GIS analysis in geomorphic susceptibility modelling<br>35. Artificial intelligence including Machine learning and Deep learning algorithms<br>36. Google Earth Engine and geomorphology<br>37. Aerial robotics including unmanned aerial vehicles (UAVs)<br>38. Internet of things (IoT), and analyzing big data (BD) in geomorphology<br>39. Geospatial analysis using AL ERAProcess modelling<br>40. Quantitative geomorphology: numerical modelling and coding.<br>41. Open-source software tools<br>42. Scripts, pictures or videos of features and links to open access tools/models