scholarly journals The Use of Semi-Automated Method for Assessing the Horizontal Positional Accuracy of Google Earth imagery

2018 ◽  
Vol 7 (4) ◽  
pp. 173
Author(s):  
Sarhat M. Adam ◽  
Abdulrahman F. Heeto
Keyword(s):  
Road Network ◽  
Accuracy Assessment ◽  
Maximum Error ◽  
Google Earth ◽  
Image Resolution ◽  
Third Body ◽  
Positional Accuracy ◽  
The Road ◽  
Automated Method ◽  
The World

Google Earth imagery is frequently used in science, engineering, and other mapping applications. However, the company owning the tool announced that the data available in its geographical products is only approximate, so its accuracy is not officially documented. The Google Earth imagery in many areas around the world has been independently checked by scholars and third body parties. The estimated accuracies are found to largely vary depending on various factors but mainly due to, the imagery source or the image resolution. Positional accuracy testing methodology may also affect the assessment results. In processing, there should be many points around the tested area in order for the comparison to be more reliable. In this paper, the horizontal accuracy assessment was carried on the Google Earth imagery in Duhok city using the traces collected via GPS in Real Time Kinematic (RTK) technique. About 38 km of trajectory was collected for the two main roads in the selected area. Via semi-automated method, the points from RTK trajectory were compared to the corresponding extracted points from the centerline of the road network of Google Earth imagery. The nearest neighboring method through buildup algorithm was considered for comparison between both sets of data. Root Mean Square Error (RMSE) and maximum error were computed for horizontal positional coordinates and found to be 1.53m and 7.76m, respectively.

scholarly journals A Morphological Model for Extracting Road Networks from High-Resolution Satellite Images

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Mohamad M. Awad
Keyword(s):  
High Resolution ◽  
Road Network ◽  
Satellite Images ◽  
Extraction Methods ◽  
Positional Accuracy ◽  
Closed Curves ◽  
New Model ◽  
Detection Techniques ◽  
The Road ◽  
High Resolution Satellite Images

Urban planning depends strongly on information extracted from high-resolution satellite images such as buildings and roads features. Nowadays, most of the available extraction techniques and methods are supervised, and they require intensive labor work to clean irrelevant features and to correct shapes and boundaries. In this paper, a new model is implemented to overcome the limitations and to correct the problems of the known and conventional techniques of urban feature extraction specifically road network. The major steps in the model are the enhancement of the image, the segmentation of the enhanced image, the application of the morphological operators, and finally the extraction of the road network. The new model is more accurate position wise and requires less effort and time compared to the traditional supervised and semi-supervised urban extraction methods such as simple edge detection techniques or manual digitization. Experiments conducted on high-resolution satellite images prove the high accuracy and the efficiency of the new model. The positional accuracy of the extracted road features compared to the manual digitized ones, the counted number of detected road segments, and the percentage of completely closed and partially closed curves prove the efficiency and accuracy of the new model.

scholarly journals AN AUTOMATED GRASS-BASED PROCEDURE TO ASSESS THE GEOMETRICAL ACCURACY OF THE OPENSTREETMAP PARIS ROAD NETWORK

2016 ◽  
Vol XLI-B7 ◽  
pp. 919-925 ◽  
Author(s):  
M. A. Brovelli ◽  
M. Minghini ◽  
M. E. Molinari
Keyword(s):  
Open Source ◽  
Road Network ◽  
Spatial Accuracy ◽  
Grass Gis ◽  
The Road ◽  
The World ◽  
Tightly Coupled ◽  
Network Quality ◽  
Gis Software

OpenStreetMap (OSM) is the largest spatial database of the world. One of the most frequently occurring geospatial elements within this database is the road network, whose quality is crucial for applications such as routing and navigation. Several methods have been proposed for the assessment of OSM road network quality, however they are often tightly coupled to the characteristics of the authoritative dataset involved in the comparison. This makes it hard to replicate and extend these methods. This study relies on an automated procedure which was recently developed for comparing OSM with any road network dataset. It is based on three Python modules for the open source GRASS GIS software and provides measures of OSM road network spatial accuracy and completeness. Provided that the user is familiar with the authoritative dataset used, he can adjust the values of the parameters involved thanks to the flexibility of the procedure. The method is applied to assess the quality of the Paris OSM road network dataset through a comparison against the French official dataset provided by the French National Institute of Geographic and Forest Information (IGN). The results show that the Paris OSM road network has both a high completeness and spatial accuracy. It has a greater length than the IGN road network, and is found to be suitable for applications requiring spatial accuracies up to 5-6 m. Also, the results confirm the flexibility of the procedure for supporting users in carrying out their own comparisons between OSM and reference road datasets.

scholarly journals ASSESSMENT OF SNOW AVALANCHE SUSCEPTIBILITY OF ROAD NETWORK - A CASE STUDY OF ALAKNANDA BASIN

2018 ◽  
Vol XLII-5 ◽  
pp. 461-468
Author(s):  
V. Singh ◽  
P. K. Thakur ◽  
V. Garg ◽  
S. P. Aggarwal
Keyword(s):  
Road Network ◽  
Thermal Emission ◽  
Armed Forces ◽  
Western Himalaya ◽  
Google Earth ◽  
Snow Avalanche ◽  
Slope Aspect ◽  
Static Parameter ◽  
The Road ◽  
Avalanche Formation

<p><strong>Abstract.</strong> Snow avalanche occurring in a micro-climatic condition causing hydro-geo (Hydrological and geological) hazard to the deployed armed forces and nearby inhabitant to the North Western Himalaya about 3000 MSL. In recent years, frequencies of snow avalanche have increase and consequently the death toll have also surged to many folds. These unavoidable occurrences not only cause road blocks which disrupts transportation connectivity in the rugged terrain of Himalaya as well as loss of infrastructure and life. Here, in this study an attempt has been made to assess the susceptibility of road network of Alaknanda Basin from snow avalanche. Potential avalanche formation zones have been generated using Analytical Hierarchical Process (AHP) of Multi-Criteria Decision Making (MCDM. Advance Thermal Emission Reflection Radiometer (ASTER) Global Digital Elevation (GDEM) 30 meter has been used to generate static parameters like slope, aspect, curvature etc. using GIS platform. ISRO-Geosphere Biosphere Program Land Use Land Cover (LULC) used as another static parameter. Weights are generated using comparison matrix and ratings to different static parameter layers assigned on the basis of field visit and literature review while the road network are digitized from Google earth. A methodology has been prepared to categorize the road stretches on the basis of potential snow avalanche formation zone including hydrological processing. Buffer zone are assigned with weights according to potential snow avalanche formation zones. Later roads are intersected with sub basin with assigned values that resulted very high avalanche potential zonation, considered as most susceptible to snow avalanche hazard.</p>

scholarly journals Mapping Drainage Structures Using Airborne Laser Scanning by Incorporating Road Centerline Information

2021 ◽  
Vol 13 (3) ◽  
pp. 463
Author(s):  
Chi-Kuei Wang ◽  
Nadeem Fareed
Keyword(s):  
Laser Scanning ◽  
Google Earth ◽  
Airborne Laser Scanning ◽  
Rural Site ◽  
Urban Site ◽  
Positional Accuracy ◽  
The Road ◽  
Airborne Laser ◽  
Public Data ◽  
Manual Methods

Wide-area drainage structure (DS) mapping is of great concern, as many DSs are reaching the end of their design life and information on their location is usually absent. Recently, airborne laser scanning (ALS) has been proven useful for DS mapping through manual methods using ALS-derived digital elevation models (DEMs) and hillshade images. However, manual methods are slow and labor-intensive. To overcome these limitations, this paper proposes an automated DS mapping algorithm (DSMA) using classified ALS point clouds and road centerline information. The DSMA begins with removing ALS ground points within the buffer of the road centerlines; the size of the buffer varies according to different road classes. An ALS-modified DEM (ALS-mDEM) is then generated from the remaining ground points. A drainage network (DN) is derived from the ALS-mDEM. Candidate DSs are then obtained by intersecting the DN with the road centerlines. Finally, a refinement buffer of 15 m is placed around each candidate DS to prevent duplicate DS from being generated in close proximity. A total area of 50 km2, including an urban site and a rural site, in Vermont, USA, was used to assess the DSMA. Based on the road functional classification scheme of the Federal Highway Administration (FHWA), the centerline information regarding FHWA roads was obtained from a public data portal. The centerline information on non-FHWA roads, i.e., private roads and streets, was derived from the impervious surface data of a land cover dataset. A benchmark DS dataset was gathered from the transport agency of Vermont and was further augmented using Google Earth Street View images by the authors. The one-to-one correspondence between the benchmark DS and mapped DS for these two sites was then established. The positional accuracy was assessed by computing the Euclidian distance between the benchmark DS and mapped DS. The mean positional accuracy for the urban site and rural site were 13.5 m and 15.8 m, respectively. F1-scores were calculated to assess the prediction accuracy. For FHWA roads, the F1-scores were 0.87 and 0.94 for the urban site and rural site, respectively. For non-FHWA roads, the F1-scores were 0.72 and 0.74 for the urban site and rural site, respectively.

scholarly journals GIS and geographical analysis of the main harbors in the world

2021 ◽  
Vol 13 (1) ◽  
pp. 639-650
Author(s):  
Aleksandar Valjarević ◽  
Dragan Radovanović ◽  
Svetislav Šoškić ◽  
Nikola Bačević ◽  
Nikola Milentijević ◽  
...  
Keyword(s):  
Road Network ◽  
East Coast ◽  
Road Traffic ◽  
Global Scale ◽  
Cluster Method ◽  
Main Road ◽  
The Road ◽  
Marine Traffic ◽  
The World ◽  
Geographical Position

Abstract This paper points out the possibilities of better exploitation of marine traffic as well as its connection with other kinds of traffic. Special attention is given to the analysis of 1,081 harbors about their availability during the year. The methods and algorithms used in GIS are buffers, cluster, method of interpolations, and network analysis. The methods used for the purpose of conducting numerical analyses are algorithms that served for the analysis of the network, its transport features, and the connectivity with harbors in terms of geospace. The main results found in this research showed that harbors have good connectivity in the first place with road traffic and after that with air and railroad traffic. According to data from 2019, all traffic lines cover 4.1 × 1015 km, and the road traffic has the most significant potential in connection with the harbors. The most connected harbors and airports are in the east coast of North America, west coast, north Europe, southern Europe, south-east Australia, a central part of Oceania, and south-east Africa. The results in the modified Likert scale between airports and harbors showed medium results. The densest road network is located in the eastern part of USA, western and central part of Europe, and east coast of China. The number of possible connected lines between main road nodes and harbors is 0.8 × 109. This type of traffic showed excellent results and connection with harbors. The number of possible connected lines per month between railroads and harbors is 1.3 × 103. This type of traffic showed low connectivity with the harbors. In the end comparison of harbors with air, road and railroad networks were established. The geographical position of harbors was analyzed, and better understanding was performed on a global scale.

scholarly journals ANALYSIS OF FREE ROAD DATA IN TANZANIA, UGANDA AND KENYA USING FREE AND OPEN SOURCE SOFTWARE

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1567-1572 ◽  
Author(s):  
S. Jovanovic ◽  
D. Jovanovic ◽  
G. Bratic ◽  
M. A. Brovelli
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Accuracy Assessment ◽  
Buffer Zone ◽  
Positional Accuracy ◽  
Human Settlements ◽  
The Road ◽  
Slow Development ◽  
Free Data ◽  
Road Data

<p><strong>Abstract.</strong> Roads are one of the most important infrastructural objects for each country. Slow development of third world countries is partially influenced by missing roads. Therefore, United Nation (UN) enlisted them inside the ninth Sustainable Development Goal (SDG) whose achievement highly relies on geospatial data. Since the authoritative data for the majority of developing countries are incomplete and unavailable, the focus of this study is on free data. The conveyed research, explained in this paper, was divided in two parts. The first one refers to completeness and positional accuracy assessment of three different road data sets (freely available). The second part was focused only on OpenStreetMap (OSM) since it showed the best results in the previous stage. Thus, OSM was used to compute (in the second part of the research) and analyse the road accessibility rate within the buffer zone of two kilometers from human settlements. To locate human settlements, raster data, representing land covers were used. Results are pointing where the infrastructure is not mapped or is not present. The complete work was done using Free and Open Source Software, which is important, since the proposed procedure can be implemented by anyone.</p>

scholarly journals Research of the current Egyptian roads service facilities placement condition

2019 ◽  
Vol 1 (52) ◽  
pp. 170-178
Author(s):  
Iryna Tkachenko ◽  
Tetyana Lytvynenko ◽  
Volodymyr Ilchenko ◽  
Mohamed Elgandour
Keyword(s):  
High Speed ◽  
Systematic Approach ◽  
Road Traffic ◽  
Google Earth ◽  
The State ◽  
Current Status ◽  
The Road ◽  
Road Users ◽  
Foreign Countries ◽  
The World

The state and provision level of road traffic participants to the objects of service on Egypt highways have been analysed. Ingeneral, there are a number of significant shortcomings regarding the systematic approach in justifying and standardizing thedistances of the road services location, considering the requirements of road users. With the help of Google Earth and realsurvey, the current status of the service objects along the Egypt highways and in foreign countries of the world has been surveyed.The obtained data show that service facilities are unevenly located in Egypt, often do not meet normative requirementsand are not characterized by complexity. Instead, in foreign countries there is even more distribution of complex servicefacilities along highways, all service facilities has transition-high-speed bands and the vast majority have separate congresses.

scholarly journals Comparison of different methods to assess the distribution of alien plants along the road network and use of Google Street View panoramas interpretation in Sicily (Italy) as a case study

2021 ◽  
Vol 9 ◽  
Author(s):  
Giulio Barone ◽  
Gianniantonio Domina ◽  
Emilio Di Gristina
Keyword(s):  
Road Network ◽  
Google Earth ◽  
Alien Plants ◽  
Aerial Images ◽  
Photo Interpretation ◽  
Aerial Photos ◽  
Google Street View ◽  
The Road ◽  
Street View ◽  
Complete Survey

The survey by foot in the field is compared to the survey from a car, the photo-interpretation of Google Street View (GSV) panoramas continuously and at intervals of 1.5 km and the photo-interpretation of Google Earth aerial images on a 10 km stretch of road in Sicily. The survey by foot was used as reference for the other methods. The interpretation of continuous GSV panoramas gave similar results as the assessment by car in terms of the number of species identified and their location, but with lower cost. The interpretation online of aerial photos allowed the identification of a limited number of taxa, but gave a good localisation for them. Interpretation of GSV panoramas, each of 1.5 km, allowed the recognition of twice as many taxa as the interpretation of aerial photos and taking half the time, but did not allow a complete localisation. None of these methods alone seems sufficient to carry out a complete survey. A mixture of different techniques, which may vary according to the available resources and the goal to be achieved, seems to be the best compromise. To further test the capabilities of the survey using the interpretation of GSV panoramas every 1.5 km along the roads, we proceeded to study the alien plants along 3500 km of the road network on the island of Sicily. This survey identified only 10% of the known species for the region, but allowed us to trace the distribution of invasive species whose distribution is currently poorly recorded.

scholarly journals Positional Accuracy Assessment of Googleearth in Riyadh

2014 ◽  
Vol 49 (2) ◽  
pp. 101-106 ◽  
Author(s):  
Ashraf Farah ◽  
Dafer Algarni
Keyword(s):  
Social Interactions ◽  
Spatial Data ◽  
Accuracy Assessment ◽  
Google Earth ◽  
Geographical Information ◽  
Small Scale ◽  
Positional Accuracy ◽  
Kingdom Of Saudi Arabia ◽  
Information Program ◽  
Private And Public

ABSTRACT Google Earth is a virtual globe, map and geographical information program that is controlled by Google corporation. It maps the Earth by the superimposition of images obtained from satellite imagery, aerial photography and GIS 3D globe. With millions of users all around the globe, GoogleEarth® has become the ultimate source of spatial data and information for private and public decision-support systems besides many types and forms of social interactions. Many users mostly in developing countries are also using it for surveying applications, the matter that raises questions about the positional accuracy of the Google Earth program. This research presents a small-scale assessment study of the positional accuracy of GoogleEarth® Imagery in Riyadh; capital of Kingdom of Saudi Arabia (KSA). The results show that the RMSE of the GoogleEarth imagery is 2.18 m and 1.51 m for the horizontal and height coordinates respectively.

scholarly journals AN AUTOMATED GRASS-BASED PROCEDURE TO ASSESS THE GEOMETRICAL ACCURACY OF THE OPENSTREETMAP PARIS ROAD NETWORK

2016 ◽  
Vol XLI-B7 ◽  
pp. 919-925
Author(s):  
M. A. Brovelli ◽  
M. Minghini ◽  
M. E. Molinari
Keyword(s):  
Open Source ◽  
Road Network ◽  
Spatial Accuracy ◽  
Grass Gis ◽  
The Road ◽  
The World ◽  
Tightly Coupled ◽  
Network Quality ◽  
Gis Software

OpenStreetMap (OSM) is the largest spatial database of the world. One of the most frequently occurring geospatial elements within this database is the road network, whose quality is crucial for applications such as routing and navigation. Several methods have been proposed for the assessment of OSM road network quality, however they are often tightly coupled to the characteristics of the authoritative dataset involved in the comparison. This makes it hard to replicate and extend these methods. This study relies on an automated procedure which was recently developed for comparing OSM with any road network dataset. It is based on three Python modules for the open source GRASS GIS software and provides measures of OSM road network spatial accuracy and completeness. Provided that the user is familiar with the authoritative dataset used, he can adjust the values of the parameters involved thanks to the flexibility of the procedure. The method is applied to assess the quality of the Paris OSM road network dataset through a comparison against the French official dataset provided by the French National Institute of Geographic and Forest Information (IGN). The results show that the Paris OSM road network has both a high completeness and spatial accuracy. It has a greater length than the IGN road network, and is found to be suitable for applications requiring spatial accuracies up to 5-6 m. Also, the results confirm the flexibility of the procedure for supporting users in carrying out their own comparisons between OSM and reference road datasets.

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