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GEOMATICA
Journal Prestige (SJR): 0.258
Number of Followers: 1  
 
Hybrid Journal Hybrid journal   * Containing 3 Open Access Open Access article(s) in this issue *
ISSN (Print) 1195-1036 - ISSN (Online) 1925-4296
Published by Canadian Science Publishing Homepage  [4 journals]
  • Precise indoor localization and mapping using mobile laser scanners: a
           scoping review

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      Authors: Amin Gharebaghi, Mir Abolfazl Mostafavi, Christian Larouche, Kamran Esmaeili, Martin Genon
      Pages: 165 - 177
      Abstract: Geomatica, Volume 75, Issue 4, Page 165-177, December 2021.
      Indoor localization and mapping are essential for a wide range of applications. The absence of GPS signals in indoor environments such as buildings, caves, and tunnels brings significant challenges for applications where accurate positioning (i.e., centimeter-level accuracy) is required. This paper presents a scoping review of the most recent studies on precise indoor localization and mapping using mobile technologies, specifically, mobile laser scanners. The scoping review allows for a comprehensive and structured review of the literature to maximize the capture of relevant information and provide reproducible results. We extracted and reported a range of information from the selected articles published since 2009, with the goal of identifying the most frequently used sensors and methods of fusing their collected observations. The results show that in the majority of studies, LiDAR is the core sensor and IMUs with 75% and odometers with 67% magnitude are the main sensors integrated with the LiDAR system to enhance the localization precision. In addition, the classical iterative closest point (ICP) algorithm with approximately 60% frequency and the extended Kalman filter (EKF) method with over 40% frequency are the main algorithms used for the scan matching and fusion of different sensor data, respectively. This scoping review also revealed the lack of mapping-systems calibration as the main limitation in over 70% of the papers analyzed.
      Citation: Geomatica
      PubDate: 2022-02-17T08:00:00Z
      DOI: 10.1139/geomat-2021-0011
      Issue No: Vol. 75, No. 4 (2022)
       
  • Exploring five indicators for the quality of OpenStreetMap road networks:
           a case study of Québec, Canada

         This is an Open Access Article Open Access Article

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      Authors: Milad Moradi, Stéphane Roche, Mir Abolfazl Mostafavi
      Pages: 178 - 208
      Abstract: Geomatica, Volume 75, Issue 4, Page 178-208, December 2021.
      OpenStreetMap (OSM) is one of the most well-known volunteered geographic information (VGI) projects that aims to produce a free-world map. However, there are serious concerns about its quality. Numerous studies have assessed the quality of OSM by comparing the OSM database with a reference database. Several researchers have proposed the use of quality indicators as variables that can describe OSM quality in regions where no reference data are available. A quality indicator is a variable that has a significant monotonic relationship with quality measures. In this study, a literature review was conducted to identify and define the main quality measures proposed for assessing the quality of linear features. Owing to limited access to current data, only three quality elements—completeness, positional accuracy, and attribute accuracy—were evaluated in this study. These quality measures were then used to assess the quality of the OSM roads in the province of Quebec. Finally, Spearman’s rank correlation coefficient test was applied to determine whether there was a significant correlation between the quality measures related to the three quality elements and the five potential quality indicators: population, average income, density of OSM roads, density of OSM buildings, and number of points of interest (POI). The main contribution of this study is testing the following hypothesis: “There is a significant correlation between the five mentioned variables and the measures related to the three quality elements”. Statistical analysis showed that in terms of completeness, the density of OSM roads and population were the best indicators; in terms of positional accuracy, population and income were the best indicators; and in terms of attribute accuracy, completeness was the best indicator. All five variables have significant correlations with the measures of the three elements of quality, except for the following two pairs (attribute accuracy, density of OSM roads) and (attribute accuracy, density of OSM buildings). This study proposes the density of OSM roads and number of POI as two new quality indicators that have not been found in the literature review.
      Citation: Geomatica
      PubDate: 2022-04-01T07:00:00Z
      DOI: 10.1139/geomat-2021-0012
      Issue No: Vol. 75, No. 4 (2022)
       
  • Building detection using a dense attention network from LiDAR and image
           data

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      Authors: Nafiseh Ghasemian, Jinfei Wang, Mohammad Reza Najafi
      Pages: 209 - 236
      Abstract: Geomatica, Volume 75, Issue 4, Page 209-236, December 2021.
      Accurate building mapping using remote sensing data is challenging because of the complexity of building structures, particularly in populated cities. LiDAR data are widely used for building extraction because they provide height information, which can help distinguish buildings from other tall objects. However, tall trees and bridges in the vicinity of buildings can limit the application of LiDAR data, particularly in urban areas. Combining LiDAR and orthoimages can help in such situations, because orthoimages can provide information on the physical properties of objects, such as reflectance characteristics. One efficient way to combine these two data sources is to use convolutional neural networks (CNN). This study proposes a CNN architecture based on dense attention blocks for building detection in southern Toronto and Massachusetts. The stacking of information from multiple previous layers was inspired by dense attention networks (DANs). DAN blocks consist of batch normalization, convolution, dropout, and average pooling layers to extract high- and low-level features. Compared with two other widely used deep learning techniques, VGG16 and Resnet50, the proposed method has a simpler architecture and converges faster with higher accuracy. In addition, a comparison with the two other state-of-the-art deep learning methods, including U-net and ResUnet, showed that our proposed technique could achieve a higher F1-score, of 0.71, compared with 0.42 for U-net and 0.49 for ResUnet.
      Citation: Geomatica
      PubDate: 2022-04-28T07:00:00Z
      DOI: 10.1139/geomat-2021-0013
      Issue No: Vol. 75, No. 4 (2022)
       
  • A deep transfer learning-based damage assessment on post-event very
           high-resolution orthophotos

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      Authors: Ghasem Abdi, Morteza Esfandiari, Shabnam Jabari
      Pages: 237 - 250
      Abstract: Geomatica, Volume 75, Issue 4, Page 237-250, December 2021.
      Post-disaster building damage assessment is an important application of remote sensing. The increasing resolution of remote sensing imaging systems and state-of-the-art deep learning networks has facilitated damage assessment. However, most existing methods in the literature concentrate on damage/non-damage classification only in specific disaster types/areas using pre- and post-event images. Furthermore, site visits are inevitable to assess the level of damage to structures. Therefore, the main objective of this study was to utilize deep transfer learning over a pre-trained network and extend it to a damage assessment framework. The network is fine-tuned to identify four different damage levels: non-damage, minor damage, major damage, and collapsed, using only post-event images taken from different disaster types/areas. To evaluate the proposed framework, we carried out three experiments on Hurricane Irma in Sint Maarten, Hurricane Dorian in Abaco Islands, and Woolsey Fire using post-event orthophotos derived from unmanned aerial vehicle (UAV) images. The results of over 80% overall accuracy confirm that with a structured learning scenario, it is possible to use transfer learning on very high-resolution remote sensing images to classify the level of structural damage.
      Citation: Geomatica
      PubDate: 2022-06-23T07:00:00Z
      DOI: 10.1139/geomat-2021-0014
      Issue No: Vol. 75, No. 4 (2022)
       
  • Three-dimensional spatial modelling of traffic-induced urban air pollution
           using the Graz Lagrangian model and GIS

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      Authors: Farimah Bakhshizadeh, Sarah Fatholahi, Lucas Prado Osco, José Marcato Junior, Jonathan Li
      Pages: 253 - 268
      Abstract: Geomatica, Volume 75, Issue 4, Page 253-268, December 2021.
      Air pollution is a significant global problem that affects climate, human, and ecosystem health. Traffic emissions are a major source of atmospheric pollution in large cities. The aim of this research was to support air quality analysis by spatially modelling traffic-induced air pollution dispersion in urban areas at the street level. The dispersion model called the Graz Lagrangian model (GRAL model) was adapted to determine the NOx concentration level based on traffic, meteorology, buildings, and street configuration data in one of Tehran’s high traffic routes. In this case, meteorological parameters such as wind speed and direction were considered significant factors. Later, using local and general auto-correlation analyses, temporal and spatial variations in the concentration of NOx were measured at different altitudes. The results showed that the average output concentration of NOx pollutants at different altitudes ranges from 64.5 to 426.6 ppb. The resulting Moran index equals to 0.7–0.9 which indicates a high level of positive spatial auto-correlation. The analysis of the local Moran index represents the overcame pollution clusters with high levels of concentration at low to medium heights and the rise in clusters with low pollution at higher heights, while there is no clear clustering in the middle sections. In addition, the study of pollutant concentration variations over time has shown that pollution peaks occur at 07:00–08:00 and 21:00–22:00.
      Citation: Geomatica
      PubDate: 2022-01-25T08:00:00Z
      DOI: 10.1139/geomat-2020-0023
      Issue No: Vol. 75, No. 4 (2022)
       
  • Fonction d’appartenance et pouvoir d’expression topologique entre
           objets aux limites fixes et floues dans le processus d’affectation des
           terres au Gabon

         This is an Open Access Article Open Access Article

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      Authors: Marius Massala, Pierre Moukeli Mbindzoukou
      Pages: 269 - 294
      Abstract: Geomatica, Volume 75, Issue 4, Page 269-294, December 2021.
      Dans le cadre d’une meilleure gestion de son territoire, le Gabon a mis en place un Programme d’Affectation des Terres (PNAT), qui vise à mieux gérer les différentes ressources que compte le pays. Mais cette gestion nécessite le règlement de plusieurs problèmes saillants. Ainsi, l’implication de plusieurs parties-prenantes dans ce secteur entraine une forte production des données géographiques hétérogènes qui se superposent les unes aux autres. Cette situation entraine des conflits de compétence entre administrations en matière d’affectation des terres. Face à cette problématique et aux limites des modèles de représentation des objets géographiques existants, nous avons jugé nécessaire de proposer une approche spécifique de modélisation qui réponde aux exigences de l’affectation des terres au Gabon. Par ailleurs, l’imbrication des zones destinées à des activités incompatibles conduit à des conflits fonciers difficiles à régler. En outre, la représentation incomplète des objets géographiques sur le terrain et une représentation spatiale ponctuelle des objets surfaciques de type communes et villages sont autant de difficultés à lever ; il en est de même de la menace sur les espèces végétales et animales protégées et la prise en compte dans la gestion, de l’évolution temporelle des activités des acteurs sur le territoire Gabonais. Afin de répondre à l’essentiel des problèmes ainsi listés, nous proposons un cadre théorique original à travers lequel nous avons premièrement étendu le modèle jaune d’œuf ( « egg–yolk » ) — imposé de fait par une décision gouvernementale — avec une fonction mathématique appelée fonction d’appartenance, permettant de déterminer le degré d’appartenance d’un objet quelconque A par rapport à un autre objet B. Cette fonction a pour intérêt, à la fois, de prendre en compte la variabilité du flou des objets géographiques manipulés dans le processus d’affectation des terres au Gabon, et d’étendre un objet géographique au-delà de ses limites connues en lui agrégeant des objets satellites. Deuxièmement, nous avons proposé la modélisation des relations topologiques binaires entre des régions spatiales aux limites fixes et celles aux limites floues sur la base du modèle à 9 intersections. Il a s’agit de croiser toutes les situations possibles en matière d’affectation des Terres au Gabon. A la suite de cela, nous avons abouti à un ensemble de relations admises que nous appelons pouvoir d’expression topologique. De ce pouvoir, nous avons déduit plusieurs définitions. Enfin, nous avons proposé un diagramme de classes qui cadre avec le processus d’affectation des terres au Gabon. L’objectif de ce travail est de proposer à terme, une solution qui permettra de régler les litiges qui pourraient en résulter.
      Citation: Geomatica
      PubDate: 2022-03-22T07:00:00Z
      DOI: 10.1139/geomat-2021-0010
      Issue No: Vol. 75, No. 4 (2022)
       
  • Soil salinity mapping using remote sensing and GIS
         This is an Open Access Article Open Access Article

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      Authors: Mahmoud Mohamed El-Sayed Gad, Mostafa H.A. Mohamed, Mervat Refaat Mohamed
      Pages: 295 - 309
      Abstract: Geomatica, Volume 75, Issue 4, Page 295-309, December 2021.
      The monitoring of soil salinity plays a vital role in agricultural society. Soil salinity causes land degradation processes, especially in arid and semi-arid regions, which influence soil properties, reduce yield production of crops, and affect infrastructure. This research produces soil salinity mapping of the East Delta in Egypt in 1995 using remote sensing technology. A Landsat 5 image taken on 26 September 1995 was used. Radiometric and atmospheric corrections for satellite data were applied. Different salinity indices (SIs) were used, such as the normalized difference salinity index, SI1, SI2, SI3, SI4, SI5, SI6, and SI7, in addition to the normalized difference vegetation index, which was used for data filtration. The field’s electrical conductivity was measured during the period from 22 to 26 September 1995 by the Japanese International Cooperation Agency. These data were used as ground truth for the correlation analysis with different indices image bands values. Simple linear regression and mean relative error were used to find the best index, which was SI5 with a 0.87 correlation with field truth data and mean relative error equal 22.7%. This index was used to produce a salinity map of the Eastern Delta with acceptable accuracy. Finally, it is concluded that using remote sensing in salinity detection and mapping is highly appreciated.
      Citation: Geomatica
      PubDate: 2022-11-01T07:00:00Z
      DOI: 10.1139/geomat-2021-0015
      Issue No: Vol. 75, No. 4 (2022)
       
 
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