Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 11, Issue 5, 2018, Pages 1363-1377

  Yokoya, Naoto ^a   Ghamisi, Pedram ^b   Xia, Junshi ^c   Sukhanov, Sergey ^d   Heremans, Roel ^d   Tankoyeu, Ivan ^d   Bechtel, Benjamin ^e   Le Saux, Bertrand ^f   Moser, Gabriele ^g   Tuia, Devis ^h  

^a Disaster Resilience Science Team   (Japan)

^b GERMAN AEROSPACE CENTER DLR   (Germany)

^c UNIVERSITY OF TOKYO   (Japan)

^d AGT Group (RandD) GmbH   (Germany)

^e UNIVERSITY OF HAMBURG   (Germany)

^f ONERA   (France)

^g UNIVERSITY OF GENOA   (Italy)

^h WAGENINGEN UNIVERSITY   (Netherlands)

Author keywords

Convolutional neural networks (CNNs); crowdsourcing; deep learning (DL); ensemble learning; image analysis and data fusion (IADF); multimodal; multiresolution; multisource; OpenStreetMap (OSM); random fields

Indexed keywords

CLASSIFICATION (OF INFORMATION); CROWDSOURCING; DEEP LEARNING; IMAGE ANALYSIS; LAND USE; NEURAL NETWORKS; REMOTE SENSING;

CONVOLUTIONAL NEURAL NETWORK; ENSEMBLE LEARNING; MULTI RESOLUTIONS; MULTI-MODAL; MULTISOURCES; OPENSTREETMAP; RANDOM FIELDS;

IMAGE FUSION;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; CROWDSOURCING; DATA PROCESSING; ENSEMBLE FORECASTING; IMAGE ANALYSIS; LAND CLASSIFICATION; LAND USE; LANDSAT; MAP; REMOTE SENSING;

EID: 85045645809     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2018.2799698     Document Type: Article

References (62)

1. I. D. Stewart and T. R. Oke, "Local climate zones for urban temperature studies, " Bull. Amer. Meteorol. Soc., vol. 93, pp. 1879-1900, 2012.
2. H. Taubenböck, T. Esch, A. Felbier, M. Wiesner, A. Roth, and S. Dech, "Monitoring urbanization in mega cities from space, " Remote Sens. Environ., vol. 117, pp. 162-176, 2012.
3. T. Esch et al., "Urban footprint processor-Fully automated processing chain generating settlement masks from global data of the TanDEM-X mission, " IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1617-1621, Nov. 2013.
4. T. Esch et al., "Earth observation-supported service platform for the development and provision of thematic information on the built environment-The TEP-Urban project, " in Proc. 2017 Joint Urban Remote Sens. Event, Dubai, UAE, Mar. 6-8, 2017.
5. M. Pesaresi et al., "Aglobal human settlement layer from optical HR/VHR RS data: Concept and first results, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 5, pp. 2102-2131, Oct. 2013.
6. European Commission, "Mapping guide for a European Urban Atlas, " Eur. Space Res. Inst., Eur. Space Agency, Frascati, Italy, Tech. Rep. ITD-0421-GSELand-TN-01, 2008.
7. B. Bechtel and C. Daneke, "Classification of local climate zones based on multiple earth observation data, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 4, pp. 1191-1202, Aug. 2012.
8. B. Bechtel et al., "Mapping local climate zones for a worldwide database of the form and function of cities, " ISPRS Int. J. Geo-Inf., vol. 4, no. 1, pp. 199-219, 2015.
9. B. Bechtel et al., "Beyond the urban mask: Local climate zones as a generic descriptor of urban areas: Potential and recent developments, " in Proc. Joint Urban Remote Sens. Event, Dubai, UAE, 2017.
10. O. Danylo, L. See, B. Bechtel, D. Schepaschenko, and S. Fritz, "Contributing to WUDAPT: A local climate zone classification of two cities in Ukraine, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 5, pp. 1841-1853, May 2016.
11. M.-L. Verdonck, A. Okujeni, S. van der Linden, M. Demuzere, R. De Wulf, and F. Van Coillie, "Influence of neighbourhood information on 'local climate zone' mapping in heterogeneous cities, " Int. J. Appl. Earth Observ. Geoinf., vol. 62, pp. 102-113, 2017.
12. N. Perera and R. Emmanuel, "A 'local climate zone' based approach to urban planning in Colombo, Sri Lanka, " Urban Clim., vol. 23, pp. 188-203, 2018.
13. Y. Xu, C. Ren, M. Cai, N. Y. Y. Edward, and T. Wu, "Classification of local climate zones using ASTER and landsat data for high-density cities, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 7, pp. 3397-3405, Jul. 2017.
14. B. Bechtel, L. See, G. Mills, and M. Foley, "Classification of local climate zones using SAR and multispectral data in an arid environment, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 7, pp. 3097-3105, Jul. 2016.
15. L. See et al., "Comparing the quality of crowdsourced data contributed by expert and non-experts, " PLoS One, vol. 8, 2013, Art. no. e69958.
16. G. M. Foody, L. See, M. van derVelde, C. Perger, C. Schill, andD. S. Boyd, "Assessing the accuracy of volunteered geographic information arising from multiple contributors to an internet based collaborative project, " Trans. GIS, vol. 17, no. 6, pp. 847-860, 2013.
17. J. C. L. Bayas et al., "Crowdsourcing in-situ data on land cover and land use using gamification and mobile technology, " Remote Sens., vol. 8, no. 11, p. 905, 2016.
18. B. Bechtel et al., "Quality of crowdsourced data on urban morphology-The human influence experiment (HUMINEX), " Urban Sci., vol. 1, no. 2, p. 15, 2017.
19. V. M. Patel, R. Gopalan, R. Li, and R. Chellappa, "Visual domain adaptation: A survey of recent advances, " IEEE Signal Process. Mag., vol. 32, no. 3, pp. 53-69, May 2015.
20. D. Tuia, C. Persello, and L. Bruzzone, "Recent advances in domain adaptation for the classification of remote sensing data, " IEEE Geosci. Remote Sens. Mag., vol. 4, no. 2, pp. 41-57, Jun. 2016.
21. L. Alparone, L. Wald, J. Chanussot, C. Thomas, P. Gamba, and L. M. Bruce, "Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S data fusion contest, " IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3012-3021, Oct. 2007.
22. F. Pacifici, F. Del Frate, W. J. Emery, P. Gamba, and J. Chanussot, "Urban mapping using coarse SAR and optical data: Outcome of the 2007 GRSS data fusion contest, " IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, pp. 331-335, Jul. 2008.
23. G. Licciardi et al., "Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S data fusion contest, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3857-3865, Nov. 2009.
24. N. Longbotham et al., "Multi-modal change detection, application to the detection of flooded areas:Outcome of the 2009-2010 data fusion contest, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 1, pp. 331-342, Feb. 2012.
25. C. Berger et al., "Multi-modal and multi-temporal data fusion: Outcome of the 2012 GRSS data fusion contest, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 3, pp. 1324-1340, Jun. 2013.
26. C. Debes et al., "Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2405-2418, Jun. 2014.
27. W. Liao et al., "Processing of thermal hyperspectral and digital color cameras: Outcome of the 2014 data fusion contest, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 8, no. 6, pp. 2984-2996, Jun. 2015.
28. M. Campos-Taberner et al., "Processing of extremely high resolution LiDAR and RGB data: Outcome of the 2015 IEEE GRSS data fusion contest-Part A: 2D contest, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 12, pp. 5547-5559, Dec. 2016.
29. A.-V. Vo et al., "Processing of extremely high resolution LiDAR and RGB data: Outcome of the 2015 IEEE GRSS data fusion contest-Part B: 3D contest, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 12, pp. 5560-5575, Dec. 2016.
30. L. Mou et al., "Multi-temporal very high resolution from space: Outcome of the 2016 IEEE GRSS data fusion contest, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 8, pp. 3435-3447, Aug. 2017.
31. F. Dell'Acqua et al., "The IEEE GRSS standardized remote sensing data website: A step towards science 2. 0 in remote sensing, " in Proc. Living Planet Symp., Prague, Czech Republic, 2016, vol. SP-740.
32. O. Conrad et al., "System for automated geoscientific analyses (SAGA) v. 2. 1. 4, " Geosci. Model Develop., vol. 8, no. 7, pp. 1991-2007, 2015.
33. D. P. Roy et al., "Landsat-8: Science and product vision for terrestrial global change research, " Remote Sens. Environ., vol. 145, pp. 154-172, 2014.
34. M. Drusch et al., "Sentinel-2: ESA's optical high-resolution mission for GMES operational services, " Remote Sens. Environ., vol. 120, pp. 25-36, 2012.
35. M. Haklay and P. Weber, "OpenStreetMap: User-generated street maps, " IEEE Pervasive Comput., vol. 7, no. 4, pp. 12-18, Oct.-Dec. 2008.
36. P. Kaiser, J. D. Wegner, A. Lucchi, M. Jaggi, T. Hofmann, andK. Schindler, "Learning aerial image segmentation from online maps, " IEEE Trans. Geosci. Remote Sens., vol. 55, no. 11, pp. 6054-6068, Nov. 2017.
37. E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, "Convolutional neural networks for large-scale remote-sensing image classification, " IEEE Trans. Geosci. Remote Sens., vol. 55, no. 2, pp. 645-657, Feb. 2017.
38. P. Lopes, C. Fonte, L. See, and B. Bechtel, "Using OpenStreetMap data to assist in the creation of LCZ maps, " in Proc. Joint Urban Remote Sens. Event, 2017, pp. 1-4.
39. R. Congalton and K. Green, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed. Boca Raton, FL, USA: CRC Press, 2009.
40. N. Yokoya, P. Ghamisi, and J. Xia, "Multimodal, multitemporal, and multisource global data fusion for local climate zones classification based on ensemble learning, " in Proc. IEEE Int. Geosci. Remote Sens. Symp., Fort Worth, TX, USA, 2017, pp. 1197-1200.
41. S. Sukhanov, I. Tankoyeu, J. Louradour, R. Heremans, D. Trofimova, and C. Debes, "Multilevel ensembling for local climate zones classification, " in Proc. IEEE Int. Geosci. Remote Sens. Symp., Fort Worth, TX, USA, 2017, pp. 1201-1204.
42. C. Souza dos Anjos, M. Goncalves Lacerda, L. do Livramento Andrade, and R. Neves Salles, "Classification of urban environments using feature extraction and random forest, " in Proc. IEEE Int. Geosci. Remote Sens. Symp., Fort Worth, TX, USA, 2017, pp. 1205-1208.
43. Y. Xu, F. Ma, D. Meng, C. Ren, and Y. Leung, "A co-training approach to the classification of local climate zones with multi-source data, " in Proc. IEEE Int. Geosci. Remote Sens. Symp., FortWorth, TX, USA, 2017, pp. 1209-1212.
44. T. Rainforth and F. Wood, "Canonical correlation forests, " arXiv:1507. 05444, 2015.
45. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system, " in Proc. SIGKDD, 2016, pp. 785-794.
46. J. J. Rodriguez and L. I. Kuncheva, "Rotation forest: A new classifier ensemble method, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 10, pp. 1619-1630, Oct. 2009.
47. R. Richter and D. Schläpfer, Atmospheric/Topographic Correction for Satellite Imagery: ATCOR 2/3 Users Guide, German Aerosp. Center (DLR), Wessling, Germany, 2016.
48. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles, " IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 480-491, Mar. 2005.
49. J. Xia, P. Du, X. He, and J. Chanussot, "Hyperspectral remote sensing image classification based on rotation forest, " IEEE Geosci. Remote Sens. Lett., vol. 11, no. 1, pp. 239-243, Jan. 2014.
50. J. Xia, J. Chanussot, P. Du, and X. He, "Spectral-spatial classification for hyperspectral data using rotation forests with local feature extraction and Markov random fields, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2532-2546, May 2015.
51. J. Xia, N. Yokoya, and A. Iwasaki, "Hyperspectral image classification with canonical correlation forests, " IEEE Trans. Geosci. Remote Sens., vol. 55, no. 1, pp. 421-431, Jan. 2017.
52. C. Ren, M. Cai, R. Wang, Y. Xu, and E. Ng, "Local climate zone (LCZ) classification using the world urban database and access portal tools (WUDAPT) method:Acase study inWuhan and Hangzhou, " in Proc. Int. Conf. Countermeasure Urban Heat Islands, Singapore, May 2016.
53. "Spaceanalyzer-indices. " [Online]. Available: www. spaceanalyzer. com
54. S. Oshigami et al., "Mineralogical mapping of southern Namibia by application of continuum-removal MSAM method to the HyMap data, " Int. J. Remote Sens., vol. 34, no. 15, pp. 5282-5295, 2013.
55. J. B. Lee, A. S. Woodyatt, andM. Berman, "Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform, " IEEE Trans. Geosci. Remote Sens., vol. 28, no. 3, pp. 295-304, May 1990.
56. A. Merentitis, C. Debes, and R. Heremans, "Ensemble learning in hyperspectral image classification: Toward selecting a favorable bias-variance tradeoff, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 1089-1102, Apr. 2014.
57. D. Marcos, M. Volpi, N. Komodakis, and D. Tuia, "RotEqNet: Rotation equivariant vector field networks, " in Proc. Int. Conf. Comput. Vis., Venice, Italy, 2017.
58. G. Moser, S. B. Serpico, and J. A. Benediktsson, "Land-cover mapping by Markov modeling of spatial-contextual information, " Proc. IEEE, vol. 101, no. 3, pp. 631-651, Mar. 2013.
59. N. V. Chawla, L. O. Hall, K. W. Bowyer, andW. P. Kegelmeyer, "SMOTE: Synthetic minority oversampling technique, " J. Artif. Intell. Res., vol. 16, pp. 321-357, 2002.
60. S. Sukhanov, A. Merentitis, C. Debes, J. Hahn, and A. M. Zoubir, "Bootstrap-based SVM aggregation for class imbalance problems, " in Proc. Eur. Signal Process. Conf., 2015, pp. 165-169.
61. L. Gómez-Chova, D. Tuia, G. Moser, and G. Camps-Valls, "Multimodal classification of remote sensing images: A review and future directions, " Proc. IEEE, vol. 103, no. 9, pp. 1560-1584, Sep. 2015.
62. S. Lefèvre, D. Tuia, J. D. Wegner, T. Produit, and A. S. Nassar, "Towards seamless multi-view scene analysis from satellite to street-level, " Proc. IEEE, vol. 105, no. 10, pp. 1884-1899, Oct. 2017.