3D Models for Spatial Analysis and Landscape Visualization

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 5096

Special Issue Editor


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Guest Editor
Faculty of Geography, Babes Bolyai University, Cluj-Napoca, Romania
Interests: GIS spatial analysis; geoinformatics; geomatics; remote sensing; environmental impact; digital cartography
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Special Issue Information

Dear Colleagues,

Three-dimensional generation, representation, and visualization of spatial reality by integrating georeferenced databases into GIS has proved highly applicable in many fields when aiming to analyze process or phenomenon dynamics or simulate the integrated development of territory, based on virtual reality. The diverse applicability of 3D models in scientific research entails different types of management, depending on the purpose and software used in analysis, thus leading to a wide variety of output database types and standardized structures, which would be integrated into specific platforms. However, the greatest challenge is to successfully create databases and construct methodologies to achieve standardized 3D models to be used for visualization and in modeling for integrated territorial analysis and derivation of secondary databases. Challenges are mainly related to the processing capabilities of computing technology, vector and raster data acquisition, possible process errors, and relation and compatibility of spatial digital data that are used for generating the final 3D models. All these emphasize the difficulties in the realization of 3D models given the multiple factors that must be considered. Modeling is therefore subject to a certain degree of subjectivity, hence the absolute need for highly accurate and correct three-dimensional models to be created and used in specialized research.

This Special Issue aims to publish research studies on the creation of 3D models based on case studies in various fields such as geography, spatial planning, agronomy, geology, engineering, hydrology, urbanism, construction industry, virtual and augmented reality, etc. Another main purpose of this Special Issue is to highlight possible errors that can occur in the modeling process and methodology construction to achieve a standardized realization of 3D models for integrated analysis and dynamic visualization of the territory and built environment.

Assoc. Prof. Dr. Ștefan Bilașco
Guest Editor

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Keywords

  • 3D model
  • structure for motion
  • UAV
  • 3D database
  • 3D landscape mapping
  • 3D landscape dissemination
  • 3D landscape management
  • 3D landscape structure

Published Papers (1 paper)

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Research

17 pages, 5119 KiB  
Article
A Novel Method Based on Deep Learning, GIS and Geomatics Software for Building a 3D City Model from VHR Satellite Stereo Imagery
by Massimiliano Pepe, Domenica Costantino, Vincenzo Saverio Alfio, Gabriele Vozza and Elena Cartellino
ISPRS Int. J. Geo-Inf. 2021, 10(10), 697; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10100697 - 14 Oct 2021
Cited by 37 | Viewed by 4295
Abstract
The aim of the paper is to identify a suitable method for the construction of a 3D city model from stereo satellite imagery. In order to reach this goal, it is necessary to build a workflow consisting of three main steps: (1) Increasing [...] Read more.
The aim of the paper is to identify a suitable method for the construction of a 3D city model from stereo satellite imagery. In order to reach this goal, it is necessary to build a workflow consisting of three main steps: (1) Increasing the geometric resolution of the color images through the use of pan-sharpening techniques, (2) identification of the buildings’ footprint through deep-learning techniques and, finally, (3) building an algorithm in GIS (Geographic Information System) for the extraction of the elevation of buildings. The developed method was applied to stereo imagery acquired by WorldView-2 (WV-2), a commercial Earth-observation satellite. The comparison of the different pan-sharpening techniques showed that the Gram–Schmidt method provided better-quality color images than the other techniques examined; this result was deduced from both the visual analysis of the orthophotos and the analysis of quality indices (RMSE, RASE and ERGAS). Subsequently, a deep-learning technique was applied for pan sharpening an image in order to extract the footprint of buildings. Performance indices (precision, recall, overall accuracy and the F1measure) showed an elevated accuracy in automatic recognition of the buildings. Finally, starting from the Digital Surface Model (DSM) generated by satellite imagery, an algorithm built in the GIS environment allowed the extraction of the building height from the elevation model. In this way, it was possible to build a 3D city model where the buildings are represented as prismatic solids with flat roofs, in a fast and precise way. Full article
(This article belongs to the Special Issue 3D Models for Spatial Analysis and Landscape Visualization)
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