Details
| Original language | English |
|---|---|
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 35 |
| Publication status | Published - 2004 |
| Event | 20th ISPRS Congress on Technical Commission VII - Istanbul, Turkey Duration: 12 Jul 2004 → 23 Jul 2004 |
Abstract
The integration of various data sets can be the answer for geoscientific questions on the one hand, but a disadvantage on the other hand, due to the differences in representation and content. Although geoscientific data sets typically refer to the same physical data source-the earth surface-and therefore also relate to topographic objects, these data sets differ in geometry, accuracy and actuality in most cases. In former times differences between analogue maps were not as apparent as today when different data sets are overlaid in a modern GIS-application. Integrating different data sets in our case topographic data and geoscientific data-allows for a consistent representation and thus for the propagation of updates from one data set to the other. This problem leads to three steps, namely harmonisation, change detection and updating which are necessary to ensure consistency, but hardly practicable when performed manually. For a harmonization of data sets of different origin, firstly the revelation of semantic differences is required; to this end, the object catalogues are compared and semantically corresponding objects are identified. In this step, also the cardinality of possible matchings between the objects in the different representations is determined (1:1, 1:n, n:m). The identification of geometric differences between the one-layered geoscientific and the multi-layered German digital map (ATKIS) will be fulfilled in the next step. In order to identify corresponding object-pairs between the data sets, different criteria like area, shape and position are used. Due to different levels of generalisation the detection of matches between groups of objects and single objects is implemented. Corresponding objects which have been selected through semantic and geometric integration are investigated for change detection using intersection methods. The geometric differences which are visible as discrepancies in position, scale and size due to simple superimposition will lead to unsatisfying results. Therefore, the iterative closest point (ICP) algorithm is implemented to achieve the best fit between the objects. The evaluated results can be classified into three types, of which two types can be handled automatically, and for one type an automatic proposal is given by the software. This leads to a significant reduction of time and resources because the approach reduces the objects to be investigated manually to only those situations where manual intervention is inescapable. The paper gives an overview of the problem and focuses on the geometric integration, especially on the matching of groups of objects and the adaptation of the object's shape.
Keywords
- Cartography, Change detection, Geology, GIS, Integration, Soil
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
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In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 35, 2004.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Integration of geoscientific data sets and the German digital map using a matching approach
AU - Gösseln, Guido v.
AU - Sester, Monika
N1 - Funding information: This is publication no. GEOTECH-66 of the GEOTECHNOLOGIEN project funded by the Federal Ministry for Education and Research (BMBF) and the German Research Council (DFG) under contract 03F0374A.
PY - 2004
Y1 - 2004
N2 - The integration of various data sets can be the answer for geoscientific questions on the one hand, but a disadvantage on the other hand, due to the differences in representation and content. Although geoscientific data sets typically refer to the same physical data source-the earth surface-and therefore also relate to topographic objects, these data sets differ in geometry, accuracy and actuality in most cases. In former times differences between analogue maps were not as apparent as today when different data sets are overlaid in a modern GIS-application. Integrating different data sets in our case topographic data and geoscientific data-allows for a consistent representation and thus for the propagation of updates from one data set to the other. This problem leads to three steps, namely harmonisation, change detection and updating which are necessary to ensure consistency, but hardly practicable when performed manually. For a harmonization of data sets of different origin, firstly the revelation of semantic differences is required; to this end, the object catalogues are compared and semantically corresponding objects are identified. In this step, also the cardinality of possible matchings between the objects in the different representations is determined (1:1, 1:n, n:m). The identification of geometric differences between the one-layered geoscientific and the multi-layered German digital map (ATKIS) will be fulfilled in the next step. In order to identify corresponding object-pairs between the data sets, different criteria like area, shape and position are used. Due to different levels of generalisation the detection of matches between groups of objects and single objects is implemented. Corresponding objects which have been selected through semantic and geometric integration are investigated for change detection using intersection methods. The geometric differences which are visible as discrepancies in position, scale and size due to simple superimposition will lead to unsatisfying results. Therefore, the iterative closest point (ICP) algorithm is implemented to achieve the best fit between the objects. The evaluated results can be classified into three types, of which two types can be handled automatically, and for one type an automatic proposal is given by the software. This leads to a significant reduction of time and resources because the approach reduces the objects to be investigated manually to only those situations where manual intervention is inescapable. The paper gives an overview of the problem and focuses on the geometric integration, especially on the matching of groups of objects and the adaptation of the object's shape.
AB - The integration of various data sets can be the answer for geoscientific questions on the one hand, but a disadvantage on the other hand, due to the differences in representation and content. Although geoscientific data sets typically refer to the same physical data source-the earth surface-and therefore also relate to topographic objects, these data sets differ in geometry, accuracy and actuality in most cases. In former times differences between analogue maps were not as apparent as today when different data sets are overlaid in a modern GIS-application. Integrating different data sets in our case topographic data and geoscientific data-allows for a consistent representation and thus for the propagation of updates from one data set to the other. This problem leads to three steps, namely harmonisation, change detection and updating which are necessary to ensure consistency, but hardly practicable when performed manually. For a harmonization of data sets of different origin, firstly the revelation of semantic differences is required; to this end, the object catalogues are compared and semantically corresponding objects are identified. In this step, also the cardinality of possible matchings between the objects in the different representations is determined (1:1, 1:n, n:m). The identification of geometric differences between the one-layered geoscientific and the multi-layered German digital map (ATKIS) will be fulfilled in the next step. In order to identify corresponding object-pairs between the data sets, different criteria like area, shape and position are used. Due to different levels of generalisation the detection of matches between groups of objects and single objects is implemented. Corresponding objects which have been selected through semantic and geometric integration are investigated for change detection using intersection methods. The geometric differences which are visible as discrepancies in position, scale and size due to simple superimposition will lead to unsatisfying results. Therefore, the iterative closest point (ICP) algorithm is implemented to achieve the best fit between the objects. The evaluated results can be classified into three types, of which two types can be handled automatically, and for one type an automatic proposal is given by the software. This leads to a significant reduction of time and resources because the approach reduces the objects to be investigated manually to only those situations where manual intervention is inescapable. The paper gives an overview of the problem and focuses on the geometric integration, especially on the matching of groups of objects and the adaptation of the object's shape.
KW - Cartography
KW - Change detection
KW - Geology
KW - GIS
KW - Integration
KW - Soil
UR - http://www.scopus.com/inward/record.url?scp=76649085632&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:76649085632
VL - 35
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
T2 - 20th ISPRS Congress on Technical Commission VII
Y2 - 12 July 2004 through 23 July 2004
ER -