research-article

Integrating gazetteers and remote sensed imagery

Authors:

Yi YangAuthors Info & Claims

GIS '08: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems

Article No.: 26, Pages 1 - 10

https://doi.org/10.1145/1463434.1463467

Published: 05 November 2008 Publication History

Abstract

This work explores the potential for increased synergy between gazetteers and high-resolution remote sensed imagery. These two data sources are complementary. Gazetteers provide high-level semantic information about what is where but they must be manually compiled and maintained. On the other hand, imagery can be automatically acquired but only provides low-level radiometric information. We explore ways in which these two data sources can be integrated to more fully automate geographic data management. In particular, we show how gazetteers represent a rich source of semi-supervised training data for geospatial object modelling. We also describe an example of information flow in the other direction, namely, how high-resolution imagery can be used to refine the spatial extents of geospatial objects in gazetteers.

References

[1]

Alexandria Digital Library Gazetteer. 1999-. Santa Barbara CA: Map and Imagery Lab, Davidson Library, University of California, Santa Barbara. Copyright UC Regents. http://www.alexandria.ucsb.edu/gazetteer.

[2]

Flickr photo sharing. http://www.flickr.com.

[3]

Zonetag research prototype. http://zonetag.research.yahoo.com.

[4]

P. Agouris, K. Beard, G. Mountrakis, and A. Stefanidis. Capturing and modeling geographic object change: A spatiotemporal gazetteer framework. Photogrammetric Engineering & Remote Sensing, 66(10):1241--1250, 2000.

[5]

P. Agouris, S. Gyftakis, and A. Stefanidis. Using a fuzzy supervisor for object extraction within an integrated geospatial environment. International Archives of Photogrammetry and Remote Sensing, 32(III/1):191--195, 1998.

[6]

T. Bailloeul, J. Duan, V. Prinet, and B. Serra. Urban digital map updating from satellite high resolution images using GIS data as a priori knowledge. In Proceedings of the GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, pages 283--287, 2003.

[7]

E. P. Baltsavias. Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3--4):129--151, 2004.

[8]

K. Barnard, P. Duygulu, and D. Forsyth. Clustering art. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, volume 2, pages 434--441, 2001.

[9]

K. Barnard and D. Forsyth. Learning the semantics of words and pictures. In Proceedings of the IEEE International Conference on Computer Vision, volume 2, pages 408--415, 2001.

[10]

A. Baumgartner, W. Eckstein, H. Mayer, C. Heipke, and H. Ebner. Context-supported road extraction. Automatic Extraction of Man-Made Objects from Aerial and Space Images, II:299--308, 1997.

[11]

T. Berg, A. Berg, J. Edwards, M. Maire, R. White, Y.-W. Teh, E. Learned-Miller, and D. Forsyth. Names and faces in the news. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, volume 2, pages 848--854, 2004.

[12]

M. Cooper, J. Foote, A. Girgensohn, and L. Wilcox. Temporal event clustering for digital photo collections. In MULTIMEDIA '03: Proceedings of the eleventh ACM international conference on Multimedia, pages 364--373, 2003.

Digital Library

[13]

P. Doucette, P. Agouris, M. Musavi, and A. Stefanidis. Automated extraction of linear features from aerial imagery using Kohonen learning and GIS data. In ISD '99: Selected Papers from the International Workshop on Integrated Spatial Databases, Digital Inages and GIS, pages 20--33, 1999.

Digital Library

[14]

D. Hand, H. Mannila, and P. Smyth. Principles of Data Mining. The MIT Press, 2001.

Digital Library

[15]

L. L. Hill, J. Frew, and Q. Zheng. Geographic names: The implementation of a gazetteer in a georeferenced digital library. D-Lib, 5(1), 1999.

[16]

L.-J. Li, G. Wang, and L. Fei-Fei. Optimol: automatic Online Picture collecTion via Incremental MOdel Learning. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pages 1--8, 2007.

[17]

D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91--110, 2004.

Digital Library

[18]

M. Michalowski, C. A. Knoblock, K. Bayer, and B. Y. Choueiry. Exploiting automatically inferred constraint-models for building identification in satellite imagery. In Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, pages 35--42, 2007.

Digital Library

[19]

K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Trans. on Pattern Analysis and Machine Intelligence, 27(10):1615--1630, 2005.

Digital Library

[20]

M. Naaman. Eyes on the world. Computer, 39(10):108--111, Oct. 2006.

Digital Library

[21]

S. Newsam, S. Bhagavathy, and B. S. Manjunath. Object localization using texture motifs and markov random fields. In Proceedings of the IEEE International Conference on Image Processing, volume 2, pages 1049--1052, 2003.

[22]

S. Newsam, B. Sumengen, and B. Manjunath. Category-based image retrieval. In Proceedings of the IEEE International Conference on Image Processing, volume 3, pages 596--599, 2001.

[23]

S. Newsam and Y. Yang. Comparing global and interest point descriptors for similarity retrieval in remote sensed imagery. In Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 2007.

Digital Library

[24]

S. Newsam and Y. Yang. Geographic image retrieval using interest point descriptors. In Proceedings of the International Symposium on Visual Computing (ISVC), 2007.

[25]

T. Quack, U. Mönich, L. Thiele, and B. S. Manjunath. Cortina: a system for large-scale, content-based web image retrieval. In MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on Multimedia, pages 508--511, 2004.

Digital Library

[26]

V. Walter and D. Fritsch. Automatic verification of GIS data using high resolution multispectral data. International Archives of Photogrammetry and Remote Sensing, 32(III/1):485--490, 1998.

[27]

Y. Yang and S. Newsam. Comparing SIFT descriptors and Gabor texture features for classfication of remote sensed imagery. In Proceedings of the IEEE International Conference on Image Processing, 2008 (accepted for publication).

[28]

C. Zhang. Towards an operational system for automated updating of road databases by integration of imagery and geodata. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3--4):166--186, 2004.

Cited By

Yang YNewsam S(2014)Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration2014 22nd International Conference on Pattern Recognition10.1109/ICPR.2014.696(4062-4067)Online publication date: Aug-2014
https://doi.org/10.1109/ICPR.2014.696
Intagorn SLerman K(2011)Mining Geospatial Knowledge on the Social WebInternational Journal of Information Systems for Crisis Response and Management10.4018/jiscrm.20110401033:2(33-47)Online publication date: 1-Apr-2011
https://doi.org/10.4018/jiscrm.2011040103
Keßler CJanowicz KBishr MWolfson OAgrawal DLu C(2009)An agenda for the next generation gazetteerProceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/1653771.1653787(91-100)Online publication date: 4-Nov-2009
https://dl.acm.org/doi/10.1145/1653771.1653787
Show More Cited By

Index Terms

Integrating gazetteers and remote sensed imagery

Recommendations

Differentiation of Acacia koa forest stands across an elevation gradient in Hawai‘i using fine-resolution remotely sensed imagery

Koa Acacia koa forests are found across broad environmental gradients in the Hawaiian Islands. Previous studies have identified important environmental factors controlling stand structure and productivity at the plot level, but these have not been ...
Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery

This article first examines three existing methods of delineating open water features, i.e. the normalized difference water index NDWI, the modified normalized difference water index MNDWI and a method combining the near-infrared NIR band and the ...
Discrimination of Eucalyptus canopy from airborne linescanner imagery using Markov random field modelling

Remotely sensed vegetation classification would be performed more effectively if the areas of interest, such as forest canopies, could be isolated from the rest of the image (i.e. the background). This background is typically made up of features such as ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

GIS '08: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems

November 2008

559 pages

ISBN:9781605583235

DOI:10.1145/1463434

Program Chairs:
Walid G. Aref
Purdue University
,
Mohamed F. Mokbel
University of Minnesota
,
Markus Schneider
University of Florida

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

ESRI
Google Inc.
Oak Ridge National Laboratory
Microsoft: Microsoft
SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GIS '08

Sponsor:

Microsoft
SIGSPATIAL

GIS '08: 16th International Symposium on Advances in Geographic Information Systems

November 5 - 7, 2008

California, Irvine

Acceptance Rates

Overall Acceptance Rate 257 of 1,238 submissions, 21%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
228
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yang YNewsam S(2014)Semi-supervised Learning of Geospatial Objects through Multi-modal Data Integration2014 22nd International Conference on Pattern Recognition10.1109/ICPR.2014.696(4062-4067)Online publication date: Aug-2014
https://doi.org/10.1109/ICPR.2014.696
Intagorn SLerman K(2011)Mining Geospatial Knowledge on the Social WebInternational Journal of Information Systems for Crisis Response and Management10.4018/jiscrm.20110401033:2(33-47)Online publication date: 1-Apr-2011
https://doi.org/10.4018/jiscrm.2011040103
Keßler CJanowicz KBishr MWolfson OAgrawal DLu C(2009)An agenda for the next generation gazetteerProceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/1653771.1653787(91-100)Online publication date: 4-Nov-2009
https://dl.acm.org/doi/10.1145/1653771.1653787
Partyka JParveen PKhan LThuraisingham BShekhar S(undefined)Enhanced Geographically-Typed Semantic Schema MatchingSSRN Electronic Journal10.2139/ssrn.3199506
https://doi.org/10.2139/ssrn.3199506

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents