Exploring land use and land cover of geotagged social-sensing images using naive bayes classifier

Sustainability (Switzerland)

Volumn 8, Issue 9, 2016, Pages

  Sitthi, Asamaporn ^a   Nagai, Masahiko ^a   Dailey, Matthew ^a   Ninsawat, Sarawut ^a  

^a ASIAN INSTITUTE OF TECHNOLOGY   (Thailand)

Author keywords

Flickr images; Image segmentation; Land use land cover (LULC) feature extraction; Majority distribution; Naive Bayes classifier; Social sensing

Indexed keywords

ACCURACY ASSESSMENT; DIGITAL IMAGE; GEOGRAPHICAL DISTRIBUTION; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LAND COVER; LAND USE; LANDSAT THEMATIC MAPPER; MACHINE LEARNING; REMOTE SENSING; SATELLITE IMAGERY; SEGMENTATION; SOCIAL MEDIA; SPATIAL DISTRIBUTION;

EID: 84990946981     PISSN: None     EISSN: 20711050     Source Type: Journal    
DOI: 10.3390/su8090921     Document Type: Article

References (43)

1. Goodchild, M.F.; Li, L. Assuring the quality of volunteered geographic information. Spat. Stat. 2012, 1, 110-120.
2. Andrienko, G.; Andrienko, N.; Bak, P.; Kisilevich, S.; Keim, D. Analysis of community-contributed space-and time-referenced data (example of Panoramio photos). In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advance Geographic Information System (GIS'09), Seattle, WA, USA, 4-6 November 2009; pp. 540-541.
3. Chen, L.; Roy, A. Event detection from Flickr data through wavelet-based spatial analysis. In Proceedings of the 18th ACM Conference Information Knowledge Management (CIKM'09), Hong Kong, China, 2-6 November 2009; pp. 523-532.
4. Antoniou, V.; Morley, J.; Haklay, M. Web 2.0 geotagged photos: Assessing the spatial dimension of the phenomenon. Geomatica 2010, 64, 99-110.
5. Heipke, C. Crowdsourcing geospatial data. ISPRS J. Photogram. Remote Sens. 2010, 65, 550-557.
6. See, L.; Mooney, P.; Foody, G.; Bastin, L.; Comber, A.; Estima, J.; Fritz, S.; Kerle, N.; Jiang, B.; Laakso, M.; et al. Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information. ISPRS Int. J. Geoinf. 2016, 5.
7. Lee, I.; Cai, G.; Lee, K. Exploration of geotagged photos through data mining approaches. Expert Syst. Appl. 2014, 41, 397-405.
8. Majid, A.; Chen, L.; Chen, G.; Mirza, H.T.; Hussain, I.; Woodward, J. A context-aware personalized travel recommendation system based on geotagged social media data mining. Int. J. Geo. Inf. Sci. 2013, 27, 662-684.
9. Estima, J.; Painho, M. Photo based volunteered geographic information initiatives: A comparative study of their suitability for helping quality control of Corine land cover. Int. J. Agric. Environ. Inf. Syst. 2014, 5, 75-92.
10. Crooks, A.; Croitoru, A.; Stefanidis, A.; Radzikowski, J. Earthquake: Twitter as a distributed sensor system. Trans. GIS 2013, 17, 124-147.
11. Longueville, B.D.; Smith, R.S. "OMG, from here, I Can See the Flames!" A Use Case of Mining Location Based Social Networks to Acquire Spatio-Temporal Data on Forest Fires. Available online: http://dl.acm. org/citation.cfm?id=1629907 (assessed on 29 August 2016).
12. Jokar Arsanjani, J.; See, L.; Tayyebi, A. Assessing the suitability of GlobeLand30 for mapping land cover in Germany. Int. J. Digit. Earth 2016, 9, 1-19.
13. Comber, A.; See, L.; Fritz, S.; van der Velde, M.; Perger, C.; Foody, G. Using control data to determine the reliability of volunteered geographic information about land cover. Int. J. Appl. Earth Obs. Geoinf. 2013, 23, 37-48.
14. Leung, D.; Newsam, S. Proximate sensing: Inferring what-is-where from georeferenced photo collections. In Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA, 13-18 June 2010; pp. 2955-2962.
15. Leung, D.; Newsam, S. Land cover classification using geo-referenced photos. Multimedia Tools Appl. 2014, 74, 11741-11761.
16. See, L.; McCallum, I.; Fritz, S.; Perger, C.; Kraxner, F.; Obersteiner, M.; Deka Baruah, U.; Mili, N.; Ram Kalita, N. Mapping cropland in Ethiopia using crowdsourcing. Int. J. Geosci. 2013, 4, 6-13.
17. The Land Processes Distributed Active Archive Center. Available online: https://lpdaac.usgs.gov/data_ access/glovis (accessed on 2 February 2015).
18. Fritz, S.; McCallum, I.; Schill, C.; Perger, C.; Grillmayer, R.; Achard, F.; Kraxner, F.; Obersteiner, M. Geo-Wiki.Org: The use of crowdsourcing to improve global land cover. Remote Sens. 2009, 1, 345-354.
19. Torralba, A.; Oliva, A. Statistics of natural image categories. Netw. Comput. Neural Syst. 2003, 14, 391-412.
20. Meyer, G.E.; Neto, J.C. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 2008, 63, 282-293.
21. Leung, D.; Newsam, S. Can off-the-shelf object detectors be used to extract geographic information from geo-referenced social multimedia? In Proceedings of the 5th International Workshop on Location-Based Social Networks, Redondo Beach, CA, USA, 6 November 2012.
22. Kumar, R.; Arthanari, M.; Sivakumar, M. Image segmentation using discontinuity-based approach. Int. J. Multimedia Image Process. 2011, 1, 72-78.
23. Shrestha, D.S.; Steward, B.L.; Bartlett, E. Segmentation of plant from background using neural network approach. In Proceedings of the Artificial Neural Networks in Engineering Conference, St. Louis, MO, USA, 4-7 November 2001.
24. Napoleon, D.; Lakshmi Priya, U.; Revathi, P. An efficient segmentation of insects from images using clustering algorithm and thresholding techniques. Int. J. Res. Advent Technol. 2013, 1, 194-199.
25. Bindu, C.H.; Prasad, K.S. A new approach for segmentation of fused images using cluster based thresholding. Int. J. Signal Image Process. 2013, 4, 1-5.
26. Vala, H.J.; Baxi, A. A review on Otsu image segmentation algorithm. Int. J. Adv. Res. Compt. Eng. Technol. 2013, 2, 387-389.
27. Parmarl, S.P.; Shah, D.H. Otsu based segmentation for thermal image. Am. Int. J. Res. Sci. Technol. Eng. Math. 2015, 10, 190-193.
28. Gonzalez, R.C.; Woods, R.E. Digital Image Processing, 2nd ed.; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2001.
29. Chary, R.V.R.; Lakshmi, D.R.; Sunitha, K.V.N. Feature extraction methods for color image similarity. Adv. Comp. Int. J. 2012, 3, 147-157.
30. Font, D.; Tresanchez, M.; Martínez, D.; Moreno, J.; Clotet, E.; Palacín, J. Vineyard yield estimation based on the analysis of high resolution images obtained with artificial illumination at night. Sensors 2015, 15, 8284-8301.
31. Xiao, J.; Hays, J.; Russell, B.C.; Patterson, G.; Ehinger, K.A.; Torralba, A.; Oliva, A. Basic level scene understanding: Categories, attributes and structures. Front. Psychol. 2013, 4, 1-10.
32. Prat, W.K. Digital Image Processing, 3rd ed.; John Wiley and Sons Inc.: Los Altos, CA, USA, 2001.
33. Guerrero, J.M.; Pajares, G.; Montalvo, M.; Romeo, J.; Guijarro, M. Support Vector Machines for crop/weeds identification in maize fields. Expert Syst. Appl. 2012, 39, 11149-11155.
34. Giltelson, A.A.; Kaufman, Y.J.; Stark, R.; Rundquist, D. Novel algorithm for remote estimation of vegetation fraction. Remote Sens. Environ. 2002, 80, 76-87.
35. Lamm, R.D.; Slaughter, D.C.; Giles, D.K. Precision weed control for cotton. Trans. ASAE 2002, 45, 231-238.
36. Mao, W.; Wang, Y.; Wang, Y. Real-time detection of between-row weeds using machine vision. In Proceedings of the 2003 ASAE Annual International Meeting, Las Vegas, NV, USA, 27-30 July 2003.
37. Meyer, G.E.; Neto, J.C.; Jones, D.D.; Hindman, T.W. Intensified fuzzy clusters for determining plant, soil, and residue regions of interest from color images. Comput. Electron. Agric. 2004, 42, 161-180.
38. Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; Witten, I.H. WEKA data mining software: An update; SIGKDD explorations. ACM SIGKDD Explor. Newsl. 2009, 11, 10-18.
39. Wieland, M.; Pittore, M. Performance evaluation of machine learning algorithms for urban pattern recognition from multi-spectral satellite images. Remote Sens. 2014, 6, 2912-2939.
40. Powers, D.M.W. Evaluation: From precision, recall and F-factor to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2007, 2, 1-24.
41. Foody, G.M.; See, L.; Fritz, S.; Van der Velde, M.; Perger, C.; Schill, C.; Boyd, D.S. Assessing the accuracy of volunteered geographic information arising from multiple contributors to an internet based collaborative project. Trans. GIS 2013, 17, 847-860.
42. Adam, A.H.M.; Elhag, A.M.H.; Salih, A.M. Accuracy assessment of land use & land cover classification (LU/LC), case study of Shomadi area, Renk County, Upper Nile State, South Sudan. Int. J. Sci. Res. Publ. 2013, 3, 1-6.
43. Calbó, J.; Jeff, S. Feature extraction from whole-sky ground-based images for cloud-type recognition. J. Atmos. Ocean Technol. 2008, 1, 3-14.