1Chapter 2 Quality Control for Object-Based Spatial Data

2009 ◽  
pp. 295-316
Keyword(s):  
Quality Control ◽  
Spatial Data ◽  
Object Based

Extended Geometric Filter for Reconstruction as a Basis for Computational Inspection

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
Alexander Miropolsky ◽  
Anath Fischer
Keyword(s):  
Quality Control ◽  
Computer Model ◽  
Large Scale ◽  
Manufacturing Industry ◽  
Digital Design ◽  
Reconstruction Methods ◽  
Object Based ◽  
Inspection Process ◽  
Important Quality ◽  
Scan Data

The inspection of machined objects is one of the most important quality control tasks in the manufacturing industry. Contemporary scanning technologies have provided the impetus for the development of computational inspection methods, where the computer model of the manufactured object is reconstructed from the scan data, and then verified against its digital design model. Scan data, however, are typically very large scale (i.e., many points), unorganized, noisy, and incomplete. Therefore, reconstruction is problematic. To overcome the above problems the reconstruction methods may exploit diverse feature data, that is, diverse information about the properties of the scanned object. Based on this concept, the paper proposes a new method for denoising and reduction in scan data by extended geometric filter. The proposed method is applied directly on the scanned points and is automatic, fast, and straightforward to implement. The paper demonstrates the integration of the proposed method into the framework of the computational inspection process.

Extended Geometric Filter for Reconstruction as a Basis for Computational Inspection

2008 ◽  
Author(s):  
Alexander Miropolsky ◽  
Anath Fischer
Keyword(s):  
Quality Control ◽  
Computer Model ◽  
Large Scale ◽  
Manufacturing Industry ◽  
New Method ◽  
Reconstruction Methods ◽  
Object Based ◽  
Inspection Process ◽  
Important Quality ◽  
Scan Data

Inspection of machined objects is one of the most important quality control tasks in the manufacturing industry. Contemporary scanning technologies have provided the impetus for the development of computational inspection methods, where the computer model of the manufactured object is reconstructed from the scan data, and then verified against its design computer model. Scan data, however, is typically very large scale (i.e. many points), unorganized, noisy and incomplete. Therefore, reconstruction is problematic. To overcome the above problems the reconstruction methods may exploit diverse feature data, that is, diverse information about the properties of the scanned object. Based on this concept, the paper proposes a new method for de-noising and reduction of scan data by Extended Geometric Filter (EGF). The proposed method is applied directly on the scanned points and is automatic, fast and straightforward to implement. The paper demonstrates the integration of the proposed method into the framework of the computational inspection process.

scholarly journals Creation (updating) digital topographic maps for the forming the main state topographic map

2021 ◽  
Vol 41 (I) ◽  
pp. 113-122
Author(s):  
N. LAZORENKO-HEVEL ◽  
◽  
Yu. KARPINKYI ◽  
D. KIN ◽  
◽  
...  
Keyword(s):  
Quality Control ◽  
Spatial Data ◽  
Knowledge Bases ◽  
International Standards ◽  
Topographic Maps ◽  
Practical Significance ◽  
Topographic Map ◽  
Topological Relations ◽  
The Creation ◽  
Automated Quality Control

Purpose. The purpose of the article is to research the peculiarities of creation (updating) of digital topographic maps at the scale of 1:50 000/1:10 000 which would satisfy the requirements for the development of the seamless Topographic Database of the Main State Topographic Map at the scale of 1:50 000. Methodology. The basis for the research is the analysis of the possibilities of applying the theory of databases and knowledge bases, International Standards and specifications and vectorization method. Results. The article examines the peculiarities of creation (updating) digital topographic maps of the scale 1:50000 for the formation of the Main State Topographic Map of Ukraine for the purpose of the creation and maintain the seamless topographic database for national needs, which is located on the Geoportal to ensure the relevance of a single digital topographic basis by topographical monitoring of the territories and for the development of the National Spatial Data Infrastructure in Ukraine. The rules of topological relations between features of the digital topographic maps of the scale 1:50 000 are also defined and given. The peculiarities of providing automated quality control of updated digital topographic maps are investigated. The creation of the seamless Topographic Database of the Main State Topographic Map in the conditions of transfer the cartographic paradigm to geoinformation creates new requirements for the creation (updating) of digital topographic maps of the scale 1:50 000/10 000: creation of spatial schemes, description of the internal design of models and rules of digital description of geospatial features, unification of the features catalog and their attributes, as well as rules of topology between topographic features to ensure topological consistency of geometry in accordance with standards and specifications; creation of the “Validate” software package for checking of created (updated) digital topographic maps at a scale of 1:50 000/10 000 to ensure automated quality control of updated digital topographic maps; creation of new virtual and associated features in the TDB of the Main State Topographic Map. This will increase the intellectual level of geospatial data creation. Scientific novelty and practical significance. The creation of the Main Topographic Map Topographic Database takes into account the use of new virtual and associated features, the use of rules of topological relations between digital topographic map features, providing automated quality control of updated digital topographic maps.

scholarly journals KLASIFIKASI MANGROVE BERBASIS OBJEK DAN PIKSEL MENGGUNAKAN CITRA SENTINEL-2B DI SUNGAI LIONG, BENGKALIS, PROVINSI RIAU

2018 ◽  
Vol 10 (3) ◽  
pp. 601-615
Author(s):  
. Rosmasita ◽  
Vincentius P. Siregar ◽  
Syamsul B. Agus
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Spatial Data ◽  
Data Availability ◽  
Support Vector ◽  
Object Based ◽  
Svm Algorithm ◽  
Aerial Vehicle ◽  
Increase In Accuracy

ABSTRAK Penelitian pemetaan mangrove di Sungai Liong, Bengkalis Provinsi Riau sangat terbatas, sehingga ketersediaan data spasial di wilayah ini masih sangat terbatas. Pemanfaatan citra satelit dapat dijadikan alternatif dalam menyediakan data spasial secara efektif dan efesien. Penelitian ini bertujuan untuk memetakan mangrove sampai tingkat komunitas menggunakan citra sentinel 2B dengan metode klasifikasi berbasis objek/OBIA dan membandingkannya dengan teknik klasifikasi berbasis piksel. Algoritma yang digunakan pada penelitian ini adalah support vector machine (SVM). Pengembangan skema klasifikasi mangrove pada penelitian ini di bagi menjadi 2 level, yaitu kelas penutup lahan di sekitar mangrove dan kelas komunitas mangrove. Data yang digunakan untuk klasifikasi kelas penutup lahan adalah data foto udara yang diperoleh dengan menggunakan pesawat tanpa awak (unmanned aerial vehicle/UAV) dan untuk klasifikasi komunitas menggunakan data transek tahun 2013. Akurasi keseluruhan  (OA) yang diperoleh untuk klafikasi penutup lahan mangrove dengan kedua teknik klasifikasi berbasis objek dan piksel berturut-turut adalah 78,7% dan 70,9%. Sedangkan akurasi keseluruhan (OA) untuk klasifikasi komunitas mangrove berbasis objek dan piksel berutru-turut yaitu 76,6% dan 75,0%. Sekitar 7,8% peningkatan akurasi pemetaan penutup lahan dan sekitar 1,6% peningkatan akurasi pemetaan komunitas mangrove yang diperoleh dengan metode klasifikasi berbasis objek. ABSTRACTResearch on mangrove mapping at the Liong River Bengkalis Riau Province was very limited, therefore the spatial data availability of mangrove in Liong River is also very limited. The use of satellite remote sensing to map mangrove has become widespread as it can provide accurate, effecient, and repeatable assessments. The purposed of this study was to map mangrove at the community level using sentinel 2B imagery based on object-based classification method (OBIA) and it compared pixel-based classification at Liong River, Bengkalis, Riau Provinc. This study was used support vector machine (SVM) algorithm. The scheme classification use is that land cover and mangrove community. The classification data of land cover was collected using unmanned aerial vehicle (UAV) and community mangrove was using transect data of 2013. The result of land cover classification and community mangrove indicated that object-based classification technique was better than pixel-based classification. The highest an overall accuracy of land cover is 78.7% versus 70.9%, whereas mangrove community is 76.6 versus 75.0%. Approximately 7.8% increase in accuracy can be achieved by object-based method of classification for land cover and 1.6% for mangrove community.

scholarly journals The Face of the Banjarbaru City Wetlands in Last Four Decades

2019 ◽  
Vol 6 (2) ◽  
pp. 130
Author(s):  
Syam'ani Syam'ani ◽  
Abdi Fithria ◽  
Eva Prihatiningtyas
Keyword(s):  
Spatial Distribution ◽  
Spatial Data ◽  
Central Government ◽  
Reduction Rate ◽  
Schedule Algorithm ◽  
Landsat 8 ◽  
Linear Pattern ◽  
The Past ◽  
Object Based ◽  
The Face

The change of Banjarbaru city status into the central government of South Kalimantan Province, has the potential to increase the need for land. This directly affects wetlands conversion activities into other forms of land closure. This research aims to map the spatial distribution of wetlands, and the spatial distribution of wetlands conversion existing in Banjarbaru City in every decade over the last four decades, ie from the 1970s to the present. Wetlands spatial data are extracted from multitemporal satellite imagery, Landsat 5 in 1973, Landsat 5 in 1989, Landsat 5 in 1997, Landsat 5 in 2007, and Landsat 8 in 2016. The method used to extract wetlands is Object Based Image Analysis (OBIA), with Full Lambda-Schedule algorithm. The research results show that over the past last decades, the total area of Banjarbaru City's wetlands has been reduced continuously. The average total reduction rate is 534.53 hectares per decade or about 53.5 hectares per year, with a linear pattern over the past four decades.

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