Advances in Geoinformatics

Geoinformatics is a scientific field primarily within the domains of Computer Science and technical geography. It focuses on the programming of applications, spatial data structures, and the analysis of objects and space-time phenomena related to the surface and underneath of Earth and other celestial bodies. The field develops software and web services to model and analyse spatial data, serving the needs of geosciences and related scientific and engineering disciplines. The term is often used interchangeably with Geomatics, although they are not exactly same. The field of geomatics is a comprehensive discipline encompassing both geodesy and geoinformatics, thus offering a more extensive scope.

In the present book, ten typical literatures about geoinformatics published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on geoinformatics. We hope this book can demonstrate advances in geoinformatics as well as give references to the researchers, students and other related people.

Sample Chapter(s)
Preface (57 KB)
Components of the Book:
  • Chapter 1
    Delineation of Groundwater Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas, Morocco
  • Chapter 2
    Using the Capabilities of AI in the Life Cycle of a GIS System
  • Chapter 3
    Use of GIS for Preventive Mammography Screening in Relation to the Change in Age Bracket by the National Health Fund
  • Chapter 4
    Digital solidarity through spatial data – an EU and African perspective
  • Chapter 5
    On the Development of a Dataset Publication Guideline: Data Repositories and Keyword Analysis in ISPRS Domain
  • Chapter 6
    Above-Ground Forest Biomass Estimation using Multispectral LiDAR Data in a Multilayered Coniferous Forest
  • Chapter 7
    The BioWhere Project: Unlocking the Potential of Biological Collections Data
  • Chapter 8
    Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization in Geoinformatics
  • Chapter 9
    Multi-Criteria Comparative Analysis of GIS Class Systems
  • Chapter 10
    Comparative Characteristics of GIS Using the AHP Method
Readership: Students, academics, teachers, and other people attending or interested in Geoinformatics.
Jerzy Stanik
litary University of Technology, Faculty of Cybernetics, Warsaw, Poland

Nikos Georgopoulos
Aristotle University of Thessaloniki, Greece

Saâd Soulaimani
Resources Valorization, Environment and Sustainable Development Research Team (RVESD), Department of Mines, Mines School of Rabat, Rabat, Morocco

and more...
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