Details
| Original language | English |
|---|---|
| Pages (from-to) | 233-238 |
| Number of pages | 6 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 1 |
| Publication status | Published - 16 Jul 2012 |
| Event | 22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012 - Melbourne, Australia Duration: 25 Aug 2012 → 1 Sept 2012 |
Abstract
Data in Geo Information Systems (GIS) is used for map services and various applications. Thus, quality assessment on a regular basis is required to keep the data up-to-date. In this paper we focus on one key reasons for updates: incorrect object borders. State of the art systems semi-automatically analyse up-to-date satellite image data to narrow down areas that have to be considered for GIS updates. Often resources are limited and only data from one point in time is available that is compared to the data. Rule based systems are required to bridge the gap between GIS specifications and results from image analysis. We present a system that can find areas of change without any manual configuration. Our approach automatically learns about important aspects of GIS specifications by analysing correct GIS objects. In potentially out-dated GIS data still a majority of objects is unchanged. Thus, we derive an model for normality (Combining double low line correctness) by evaluating the coherence of relations between GIS objects and image analysis results. We synthesise changes at GIS object borders and analyse the impact on normality. In an evolutionary optimisation we determine areas of change that are rated with a significance value. We show that we can find 83% of all relevant update areas with a precision of 0.18, not considering the significance of changes. Including significance we can push the precision to 0.26 while still finding 77% of all relevant update areas.
Keywords
- Change Detection, Land Cover, Semi-automation, Updating
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Physics and Astronomy(all)
- Instrumentation
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 1, 16.07.2012, p. 233-238.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Mono-temporal gis update assistance system based on unsupervised coherence analysis and evolutionary optimisation
AU - Becker, C.
AU - Ostermann, J.
AU - Pahl, M.
PY - 2012/7/16
Y1 - 2012/7/16
N2 - Data in Geo Information Systems (GIS) is used for map services and various applications. Thus, quality assessment on a regular basis is required to keep the data up-to-date. In this paper we focus on one key reasons for updates: incorrect object borders. State of the art systems semi-automatically analyse up-to-date satellite image data to narrow down areas that have to be considered for GIS updates. Often resources are limited and only data from one point in time is available that is compared to the data. Rule based systems are required to bridge the gap between GIS specifications and results from image analysis. We present a system that can find areas of change without any manual configuration. Our approach automatically learns about important aspects of GIS specifications by analysing correct GIS objects. In potentially out-dated GIS data still a majority of objects is unchanged. Thus, we derive an model for normality (Combining double low line correctness) by evaluating the coherence of relations between GIS objects and image analysis results. We synthesise changes at GIS object borders and analyse the impact on normality. In an evolutionary optimisation we determine areas of change that are rated with a significance value. We show that we can find 83% of all relevant update areas with a precision of 0.18, not considering the significance of changes. Including significance we can push the precision to 0.26 while still finding 77% of all relevant update areas.
AB - Data in Geo Information Systems (GIS) is used for map services and various applications. Thus, quality assessment on a regular basis is required to keep the data up-to-date. In this paper we focus on one key reasons for updates: incorrect object borders. State of the art systems semi-automatically analyse up-to-date satellite image data to narrow down areas that have to be considered for GIS updates. Often resources are limited and only data from one point in time is available that is compared to the data. Rule based systems are required to bridge the gap between GIS specifications and results from image analysis. We present a system that can find areas of change without any manual configuration. Our approach automatically learns about important aspects of GIS specifications by analysing correct GIS objects. In potentially out-dated GIS data still a majority of objects is unchanged. Thus, we derive an model for normality (Combining double low line correctness) by evaluating the coherence of relations between GIS objects and image analysis results. We synthesise changes at GIS object borders and analyse the impact on normality. In an evolutionary optimisation we determine areas of change that are rated with a significance value. We show that we can find 83% of all relevant update areas with a precision of 0.18, not considering the significance of changes. Including significance we can push the precision to 0.26 while still finding 77% of all relevant update areas.
KW - Change Detection
KW - Land Cover
KW - Semi-automation
KW - Updating
UR - http://www.scopus.com/inward/record.url?scp=84924650285&partnerID=8YFLogxK
U2 - 10.5194/isprsannals-I-4-233-2012
DO - 10.5194/isprsannals-I-4-233-2012
M3 - Conference article
AN - SCOPUS:84924650285
VL - 1
SP - 233
EP - 238
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
T2 - 22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012
Y2 - 25 August 2012 through 1 September 2012
ER -