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Geospatial Technology: Modern Applications and Their Impact, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1058

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Guest Editor
School of Applied Engineering and Technology, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: planetary surfaces; geophysics; geodesy; geographic information science; remote sensing; geoAI; autonomous mobile mapping systems; point cloud processing; ionospheric dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geospatial technology, including sensors, cameras, lasers, telescopes, unmanned aerial vehicles, autonomous mobile mapping robots, geographic information sciences/systems, remote sensing, and global navigation satellite systems, is growing at a rapid pace and is now informing decision-makers on topics such as industrial engineering, biodiversity conservation, climate change, ecological and agricultural monitoring, humanitarian relief, and many more. Geospatial technology, which takes the possibilities of humankind to all-new levels of advancement, usually generates large and complex datasets for reality capture that call for the use of all aspects of artificial intelligence/augmented intelligence (AI) as well as innovative and straightforward visualization platforms to reveal meaningful information. The scope of geospatial technology applications embraces every sphere or industry where location-based data play a crucial role in answering major social questions related, but not limited to, the environment, climate change, crisis management, sustainable development, civil infrastructure/assets mapping, and structural health monitoring. The goal of this Special Issue on “Geospatial Technology: Modern Applications and Their Impact, 2nd Edition” is to provide a unique forum for the dissemination of theories and innovative applications of geospatial data/imagery analysis and visualization across several diverse fields, including geophysics, geography, agriculture, ecology, law enforcement, mapping, engineering, marine science, meteorology, as well as those of an interdisciplinary nature. Lastly, papers describing theoretical results will also expand on their practical utility.

Dr. Laramie Potts
Guest Editor

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Keywords

  • reality capture
  • AI
  • machine learning
  • visualization
  • multi-criteria decision analysis
  • geoAI
  • big data

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Published Papers (1 paper)

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Research

28 pages, 5677 KB  
Article
Knowledge-Based Approach for Contextual Landsystem Identification: A Conceptual Model and Graph-Based Software, with an Application to Mountain Glacial Valleys
by Hariniaina Ramiaramanana, Eric Guilbert, Bernard Moulin and Patrick Lajeunesse
Appl. Sci. 2025, 15(22), 12039; https://doi.org/10.3390/app152212039 - 12 Nov 2025
Viewed by 580
Abstract
A landform is a physical feature of the Earth’s surface with its own recognizable shape. Most current automated landform identification methods use Object-Based Image Analysis (OBIA) techniques. Such methods segment the terrain into landform elements and assemble them into topographic objects and landforms. [...] Read more.
A landform is a physical feature of the Earth’s surface with its own recognizable shape. Most current automated landform identification methods use Object-Based Image Analysis (OBIA) techniques. Such methods segment the terrain into landform elements and assemble them into topographic objects and landforms. Usually, these methods are specific to the landform to be identified. However, geomorphologist experts can contextually recognize any landform on the Earth’s surface in relation to its environment. They have a holistic view of the landscape, adopting a physiographic approach for the interpretation of the observed regions, the objects that they contain and their relationships. Moreover, geomorphological processes leave marks on the Earth’s surface that enable geomorphologists to identify homogeneous regions by recognizing features known as structural elements. In this paper, we show that the physiographic approach can be formalized and that the context of appearance of a landform and its association with other types of landforms can be represented as a landsystem. We propose a conceptual model that organizes the main concepts and relationships characterizing the physiographic approach: they are used to formalize landsystems, landforms and structural elements. The approach is illustrated using a case study of the identification of landsystems characteristic of mountainous glacial valleys. We developed a software to automatically identify landsystems, in a way that is compatible with the geomorphologists’ physiographic approach. The core of this system is a knowledge base implemented as a Neo4j graph database. We also provide details about the logical transformation of the conceptual model and the corresponding ontologies in Noe4j structures. The tool automates the identification of landsystems in accordance with geomorphological practices, facilitating the integration of expert knowledge in the computational workflows. Full article
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