scholarly journals A Photogrammetric Method for Spatial Data Extraction from Google Earth and Improvement with Precision Analysis

2018 ◽  
Vol 4 (4) ◽  
pp. 886
Author(s):  
Sadegh Karimi ◽  
Ehsan Khorrambakht
Keyword(s):  
Spatial Data ◽  
Data Extraction ◽  
Water Channel ◽  
Road Construction ◽  
Google Earth ◽  
Precision Analysis ◽  
Closed Range ◽  
Ground Control Points ◽  
Photogrammetric Method ◽  
Field Works

Topography maps are crucial for civil engineering projects, such as road construction, water channel construction, urban construction, and mining. Here we present a method which enables us to extract topographical map via modeling Google Earth and some field works. In this method, first, we model Google Earth as an object with closed-range photogrammetric method in the Agisoft Photoscan. Through some field works, we measured twenty-two points including twelve ground control points (GCP) and ten independent check points (ICP). Due to these GCPs, we were able to transform our model to real world with global polynomial and multi-quadratic equations and ICPs were used for precision analysis. This method is easy and cheap to obtain spatial data and the accuracy is sufficient for research requirements.

Evaluation of the expediency of using thermal i magery data for decryption exogenous processes and vegetation

2018 ◽  
Vol 933 (3) ◽  
pp. 52-62
Author(s):  
V.S. Tikunov ◽  
I.A. Rylskiy ◽  
S.B. Lukatzkiy
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Data Extraction ◽  
Spectral Band ◽  
Information Provision ◽  
Lidar Data ◽  
Thermal Imagery ◽  
Scanning Lidar ◽  
Field Works ◽  
Object Features

Innovative methods of aerial surveys changed approaches to information provision of projecting dramatically in last years. Nowadays there are several methods pretending to be the most efficient for collecting geospatial data intended for projecting – airborne laser scanning (LIDAR) data, RGB aerial imagery (forming 3D pointclouds) and orthoimages. Thermal imagery is one of the additional methods that can be used for projecting. LIDAR data is precise, it allows us to measure relief even under the vegetation, or to collect laser re-flections from wires, metal constructions and poles. Precision and completeness of the DEM, produced from LIDAR data, allows to define relief microforms. Airborne imagery (visual spectrum) is very widespread and can be easily depicted. Thermal images are more strange and less widespread, they use different way of image forming, and spectral features of ob-jects can vary in specific ways. Either way, the additional spectral band can be useful for achieving additional spatial data and different object features, it can minimize field works. Here different aspects of thermal imagery are described in comparison with RGB (visual) images, LIDAR data and GIS layers. The attempt to estimate the feasibility of thermal imag-es for new data extraction is made.

scholarly journals THE CULTURAL HERITAGE SPATIAL DATA SET OF KLAIPĖDA OLD TOWN / SAUGOMŲ KLAIPĖDOS SENAMIESČIO OBJEKTŲ ERDVINIŲ DUOMENŲ RINKINYS / НАБОР ПРОСТРАНСТВЕННЫХ ДАННЫХ ОХРАНЯЕМЫХ ОБЪЕКТОВ СТАРОГО ГОРОДА КЛАЙПЕДЫ

2012 ◽  
Vol 37 (4) ◽  
pp. 172-176
Author(s):  
Lina Kuklienė ◽  
Dainora Jankauskienė ◽  
Indrius Kuklys
Keyword(s):  
Cultural Heritage ◽  
Spatial Data ◽  
Road Construction ◽  
Heritage Management ◽  
Data Set ◽  
Management Planning ◽  
Cultural Heritage Management ◽  
Input Devices ◽  
Relevant Point ◽  
Cultural Heritage Objects

The purpose of the thesis is to analyze the main geodetic databases of Lithuania and to create a geodetic database of cultural heritage objects in Klaipėda using program ArcGIS 9.3. The problem is that the geodetic database storing graphical and attributive information about cultural heritage in Klaipeda city has not been created yet. Thus, in order to incorporate GIS technologies into the management of cultural heritage, starting the creation of such a database seems to be a relevant point. The fully completed and regularly updated geodetic database can be used for cultural heritage management, planning, design, road construction, etc. Therefore, the following objectives have been set: 1) describing geo-data collection and input devices; 2) stimulating the geodetic database that introduces information about buildings, building complexes, cemeteries, locations of archaeological and cultural heritage; 3) giving a detailed description of the database creation process; 4) analyzing the need for establishing a geodetic database of cultural heritage objects in Klaipėda. Santrauka Lietuvoje GIS pagrindu sukurta daug įvairiems tikslams skirtų georeferencinių bei teminių erdvinių duomenų rinkinių. Viena iš šių rinkinių panaudojimo sričių – valstybės registruose esančių duomenų kaupimas. Tokiu principu yra sukurta Kultūros vertybių registro duomenų bazė, kurios pagrindiniai duomenys buvo panaudoti kuriant Klaipėdos miesto kultūros paveldo objektų erdvinių duomenų rinkinį. Siekiant kuo operatyviau įtraukti GIS technologijas į kultūros paveldo objektų tvarkybą, aktualu Klaipėdoje pradėti kurti kultūros paveldo objektų erdvinių duomenų rinkinį. Nuolat atnaujinamas erdvinių duomenų rinkinys palengvins įvairių sričių specialistų atliekamus kultūros paveldo objektų administravimo, teritorijų planavimo, projektavimo, kelių tiesimo ir kitus darbus. Резюме В Литве на основе ГИС для различных целей создано множество гео-ссылок, а также тематических наборов пространственных данных. Область использования одного из множеств – сбор данных, имеющихся в государственном учете. По такому принципу создана регистрационная база культурных ценностей, основные данные которой были использованы при создании набора пространственных данных объектов культурного наследия города Клайпеды. С целью оперативно обеспечить управление объектами культурного наследия технологиями ГИС следует начать создание набора пространственных данных объектов культурного наследия в Клайпеде. Полностью заполненный и постоянно обновляемый набор пространственных данных облегчит работу специалистов в различных областях: администрировании объектов культурного наследия, планировании территорий, проектировании, строительстве дорог и других.

Estimating productivity of earthmoving operations using spatial technologies1This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.

2012 ◽  
Vol 39 (9) ◽  
pp. 1072-1082 ◽  
Author(s):  
Ali Montaser ◽  
Ibrahim Bakry ◽  
Adel Alshibani ◽  
Osama Moselhi
Keyword(s):  
Real Time ◽  
Spatial Data ◽  
Data Fitting ◽  
Google Earth ◽  
Simulation Software ◽  
Distribution Functions ◽  
Automated Classification ◽  
Cycle Times ◽  
Automated Method ◽  
Spreadsheet Application

This paper presents an automated method for estimating productivity of earthmoving operations in near-real-time. The developed method utilizes Global Positioning System (GPS) and Google Earth to extract the data needed to perform the estimation process. A GPS device is mounted on a hauling unit to capture the spatial data along designated hauling roads for the project. The variations in the captured cycle times were used to model the uncertainty associated with the operation involved. This was carried out by automated classification, data fitting, and computer simulation. The automated classification is applied through a spreadsheet application that classifies GPS data and identifies, accordingly, durations of different activities in each cycle using spatial coordinates and directions captured by GPS and recorded on its receiver. The data fitting was carried out using commercially available software to generate the probability distribution functions used in the simulation software “Extend V.6”. The simulation was utilized to balance the production of an excavator with that of the hauling units. A spreadsheet application was developed to perform the calculations. An example of an actual project was analyzed to demonstrate the use of the developed method and illustrates its essential features. The analyzed case study demonstrates how the proposed method can assist project managers in taking corrective actions based on the near-real-time actual data captured and processed to estimate productivity of the operations involved.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

scholarly journals Estimation of Building Heights and DEM Accuracy Assessment Using ICESat-2 Data Products

2021 ◽  
Vol 10 (1) ◽  
pp. 37
Author(s):  
Goddu Pavan Sai Goud ◽  
Ashutosh Bhardwaj
Keyword(s):  
Spatial Data ◽  
Rural Areas ◽  
Urban Areas ◽  
Accuracy Assessment ◽  
Urban Expansion ◽  
Population Expansion ◽  
Cost Effective ◽  
Absolute Error ◽  
Google Earth ◽  
Surface Geometry

The use of remote sensing for urban monitoring is a very reliable and cost-effective method for studying urban expansion in horizontal and vertical dimensions. The advantage of multi-temporal spatial data and high data accuracy is useful in mapping urban vertical aspects like the compactness of urban areas, population expansion, and urban surface geometry. This study makes use of the ‘Ice, cloud, and land elevation satellite-2′ (ICESat-2) ATL 03 photon data for building height estimation using a sample of 30 buildings in three experimental sites. A comparison of computed heights with the heights of the respective buildings from google image and google earth pro was done to assess the accuracy and the result of 2.04 m RMSE was obtained. Another popularly used method by planners and policymakers to map the vertical dimension of urban terrain is the Digital Elevation Model (DEM). An assessment of the openly available DEM products—TanDEM-X and Cartosat-1 has been done over Urban and Rural areas. TanDEM-X is a German earth observation satellite that uses InSAR (Synthetic Aperture Radar Interferometry) technique to acquire DEM while Cartosat-1 is an optical stereo acquisition satellite launched by the Indian Space Research Organization (ISRO) that uses photogrammetric techniques for DEM acquisition. Both the DEMs have been compared with ICESat-2 (ATL-08) Elevation data as the reference and the accuracy has been evaluated using Mean error (ME), Mean absolute error (MAE) and Root mean square error (RMSE). In the case of Greater Hyderabad Municipal Corporation (GHMC), RMSE values 5.29 m and 7.48 m were noted for TanDEM-X 90 and CartoDEM V3 R1 respectively. While the second site of Bellampalli Mandal rural area observed 5.15 and 5.48 RMSE values for the same respectively. Therefore, it was concluded that TanDEM-X has better accuracy as compared to the CartoDEM V3 R1.

scholarly journals Olomouc - Possibilities of Geovisualization of the Historical City

2011 ◽  
Vol 6 ◽  
pp. 267-274
Author(s):  
Stanislav Popelka ◽  
Alžběta Brychtová
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
18Th Century ◽  
3D Visualization ◽  
3D Models ◽  
Google Earth ◽  
Point Of View ◽  
World Heritage Site ◽  
Historical Maps ◽  
The City

Olomouc, nowadays a city with 100,000 inhabitants, has always been considered as one of the most prominent Czech cities. It is a social and economical centre, which history started just about the 11th century. The present appearance of the city has its roots in the 18th century, when the city was almost razed to the ground after the Thirty years’ war and a great fire in 1709. After that, the city was rebuilt to a baroque military fortress against Prussia army. At the beginning of the 20th century the majority of the fortress was demolished. Character of the town is dominated by the large number of churches, burgher’s houses and other architecturally significant buildings, like a Holy Trinity Column, a UNESCO World Heritage Site. Aim of this project was to state the most suitable methods of visualization of spatial-temporal change in historical build-up area from the tourist’s point of view, and to design and evaluate possibilities of spatial data acquisition. There are many methods of 2D and 3D visualization which are suitable for depiction of historical and contemporary situation. In the article four approaches are discussed comparison of historical and recent pictures or photos, overlaying historical maps over the orthophoto, enhanced visualization of historical map in large scale using the third dimension and photorealistic 3D models of the same area in different ages. All mentioned methods were geolocalizated using the Google Earth environment and multimedia features were added to enhance the impression of perception. Possibilities of visualization, which were outlined above, were realized on a case study of the Olomouc city. As a source of historical data were used rapport plans of the bastion fortress from the 17th century. The accuracy of historical maps was confirmed by cartometric methods with use of the MapAnalyst software. Registration of the spatial-temporal changes information has a great potential in urban planning or realization of reconstruction and particularly in the propagation of the region and increasing the knowledge of citizens about the history of Olomouc.

scholarly journals The Design and Testing of 3DmoveR: an Experimental Tool for Usability Studies of Interactive 3D Maps

2018 ◽  
pp. 31-63 ◽  
Author(s):  
Lukáš Herman ◽  
Tomáš Řezník ◽  
Zdeněk Stachoň ◽  
Jan Russnák
Keyword(s):  
Spatial Data ◽  
Response Times ◽  
3D Visualization ◽  
User Interaction ◽  
Google Earth ◽  
Web Based ◽  
3D Environments ◽  
Camera Position ◽  
Interactive 3D ◽  
3D Scene

Various widely available applications such as Google Earth have made interactive 3D visualizations of spatial data popular. While several studies have focused on how users perform when interacting with these with 3D visualizations, it has not been common to record their virtual movements in 3D environments or interactions with 3D maps. We therefore created and tested a new web-based research tool: a 3D Movement and Interaction Recorder (3DmoveR). Its design incorporates findings from the latest 3D visualization research, and is built upon an iterative requirements analysis. It is implemented using open web technologies such as PHP, JavaScript, and the X3DOM library. The main goal of the tool is to record camera position and orientation during a user’s movement within a virtual 3D scene, together with other aspects of their interaction. After building the tool, we performed an experiment to demonstrate its capabilities. This experiment revealed differences between laypersons and experts (cartographers) when working with interactive 3D maps. For example, experts achieved higher numbers of correct answers in some tasks, had shorter response times, followed shorter virtual trajectories, and moved through the environment more smoothly. Interaction-based clustering as well as other ways of visualizing and qualitatively analyzing user interaction were explored.

scholarly journals Correction of Very High-Resolution Satellite Images using Control Points Captured by Web Map Service (WMS) server: Google Earth

2020 ◽  
Vol 9 (8) ◽  
pp. 188-194
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Google Earth ◽  
Ground Control ◽  
Control Points ◽  
Web Map ◽  
Ground Control Points ◽  
High Resolution Satellite Images ◽  
Map Scale ◽  
Map Service

The recent progress for spatial resolution of remote sensing imagery led to generate many types of Very HighResolution (VHR) satellite images, consequently, general speaking, it is possible to prepare accurate base map larger than 1:10,000 scale. One of these VHR satellite image is WorldView-3 sensor that launched in August 2014. The resolution of 0.31m makes WorldView-3 the highest resolution commercial satellite in the world. In the current research, a pan-sharpen image from that type, covering an area at Giza Governorate in Egypt, used to determine the suitable large-scale map that could be produced from that image. To reach this objective, two different sources for acquiring Ground Control Points (GCPs). Firstly, very accurate field measurements using GPS and secondly, Web Map Service (WMS) server (in the current research is Google Earth) which is considered a good alternative when GCPs are not available, are used. Accordingly, three scenarios are tested, using the same set of both 16 Ground Control Points (GCPs) as well as 14 Check Points (CHKs), used for evaluation the accuracy of geometric correction of that type of images. First approach using both GCPs and CHKs coordinates acquired by GPS. Second approach using GCPs coordinates acquired by Google Earth and CHKs acquired by GPS. Third approach using GCPs and CHKs coordinates by Google Earth. Results showed that, first approach gives Root Mean Square Error (RMSE) planimeteric discrepancy for GCPs of 0.45m and RMSE planimeteric discrepancy for CHKs of 0.69m. Second approach gives RMSE for GCPs of 1.10m and RMSE for CHKs of 1.75m. Third approach gives RMSE for GCPs of 1.10m and RMSE for CHKs of 1.40m. Taking map accuracy specification of 0.5mm of map scale, the worst values for CHKs points (1.75m&1,4m) resulted from using Google Earth as a source, gives the possibility of producing 1:5000 large-scale map compared with the best value of (0.69m) (map scale 1:2500). This means, for the given parameters of the current research, large scale maps could be produced using Google Earth, in case of GCPs are not available accurately from the field surveying, which is very useful for many users.

scholarly journals Highly Resolved Rainfall-Runoff Simulation of Retrofitted Green Stormwater Infrastructure at the Micro-Watershed Scale

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 339 ◽  
Author(s):  
Sami Towsif Khan ◽  
Fernando Chapa ◽  
Jochen Hack
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Surface Runoff ◽  
Spatial Databases ◽  
Google Earth ◽  
Urban Land Use ◽  
Small Scale ◽  
Runoff Reduction ◽  
Intense Storm ◽  
Green Stormwater Infrastructure

Green Stormwater Infrastructure (GSI), a sustainable engineering design approach for managing urban stormwater runoff, has long been recommended as an alternative to conventional conveyance-based stormwater management strategies to mitigate the adverse impact of sprawling urbanization. Hydrological and hydraulic simulations of small-scale GSI measures in densely urbanized micro watersheds require high-resolution spatial databases of urban land use, stormwater structures, and topography. This study presents a highly resolved Storm Water Management Model developed under considerable spatial data constraints. It evaluates the cumulative effect of the implementation of dispersed, retrofitted, small-scale GSI measures in a heavily urbanized micro watershed of Costa Rica. Our methodology includes a high-resolution digital elevation model based on Google Earth information, the accuracy of which was sufficient to determine flow patterns and slopes, as well as to approximate the underground stormwater structures. The model produced satisfactory results in event-based calibration and validation, which ensured the reliability of the data collection procedure. Simulating the implementation of GSI shows that dispersed, retrofitted, small-scale measures could significantly reduce impermeable surface runoff (peak runoff reduction up to 40%) during frequent, less intense storm events and delay peak surface runoff by 5–10 min. The presented approach can benefit stormwater practitioners and modelers conducting small scale hydrological simulation under spatial data constraint.

scholarly journals Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia

2020 ◽  
Vol 9 (11) ◽  
pp. 663
Author(s):  
Sanjiwana Arjasakusuma ◽  
Sandiaga Swahyu Kusuma ◽  
Raihan Rafif ◽  
Siti Saringatin ◽  
Pramaditya Wicaksono
Keyword(s):  
Spatial Data ◽  
Vegetation Index ◽  
Google Earth ◽  
Paddy Fields ◽  
Series Data ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Seamless Integration ◽  
Synthetic Aperture Radar Data ◽  
Central Java

The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.

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