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

In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference.

[1]  Naoto Yokoya,et al.  Multimodal, multitemporal, and multisource global data fusion for local climate zones classification based on ensemble learning , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[2]  Christian Debes,et al.  Ensemble Learning in Hyperspectral Image Classification: Toward Selecting a Favorable Bias-Variance Tradeoff , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Jocelyn Chanussot,et al.  Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Peijun Du,et al.  Hyperspectral Remote Sensing Image Classification Based on Rotation Forest , 2014, IEEE Geoscience and Remote Sensing Letters.

[5]  Pierre Alliez,et al.  Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Frank D. Wood,et al.  Canonical Correlation Forests , 2015, ArXiv.

[7]  Jocelyn Chanussot,et al.  Decision Fusion for the Classification of Hyperspectral Data: Outcome of the 2008 GRS-S Data Fusion Contest , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Yong Xu,et al.  Beyond the urban mask , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[9]  Jon Atli Benediktsson,et al.  Land-Cover Mapping by Markov Modeling of Spatial–Contextual Information in Very-High-Resolution Remote Sensing Images , 2013, Proceedings of the IEEE.

[10]  J. B. Lee,et al.  Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform , 1990 .

[11]  Rama Chellappa,et al.  Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.

[12]  Gabriele Moser,et al.  Multimodal Classification of Remote Sensing Images: A Review and Future Directions , 2015, Proceedings of the IEEE.

[13]  Frieke Van Coillie,et al.  Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX) , 2017 .

[14]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[15]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  William J. Emery,et al.  Urban Mapping Using Coarse SAR and Optical Data: Outcome of the 2007 GRSS Data Fusion Contest , 2008, IEEE Geoscience and Remote Sensing Letters.

[17]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[18]  Michael Bock,et al.  System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 , 2015 .

[19]  Alexandre Boulch,et al.  Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest–Part A: 2-D Contest , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Iain Stewart,et al.  Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities , 2015, ISPRS Int. J. Geo Inf..

[21]  Frieke Van Coillie,et al.  Influence of neighbourhood information on 'Local Climate Zone' mapping in heterogeneous cities , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Meng Cai,et al.  Classification of Local Climate Zones Using ASTER and Landsat Data for High-Density Cities , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[24]  Rohinton Emmanuel,et al.  A "Local Climate Zone" based approach to urban planning in Colombo, Sri Lanka , 2016 .

[25]  Thomas Esch,et al.  Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission , 2013, IEEE Geoscience and Remote Sensing Letters.

[26]  Qian Du,et al.  Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Thomas Hofmann,et al.  Learning Aerial Image Segmentation From Online Maps , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Naoto Yokoya,et al.  Hyperspectral Image Classification With Canonical Correlation Forests , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[29]  C. S. Anjos,et al.  Classification of urban environments using feature extraction and random forest , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[30]  Nikos Paragios,et al.  Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Steffen Fritz,et al.  Assessing the Accuracy of Volunteered Geographic Information arising from Multiple Contributors to an Internet Based Collaborative Project , 2013, Trans. GIS.

[32]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[33]  Abdelhak M. Zoubir,et al.  Bootstrap-based SVM aggregation for class imbalance problems , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[34]  Nikos Komodakis,et al.  Rotation Equivariant Vector Field Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[35]  Huadong Guo,et al.  A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[36]  Benjamin Bechtel,et al.  Classification of Local Climate Zones Using SAR and Multispectral Data in an Arid Environment , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Jérôme Louradour,et al.  Multilevel ensembling for local climate zones classification , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[38]  Steffen Fritz,et al.  Contributing to WUDAPT: A Local Climate Zone Classification of Two Cities in Ukraine , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[39]  Jan Dirk Wegner,et al.  Toward Seamless Multiview Scene Analysis From Satellite to Street Level , 2017, Proceedings of the IEEE.

[40]  Cidália Costa Fonte,et al.  Using OpenStreetMap data to assist in the creation of LCZ maps , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[41]  Qian Du,et al.  Multi-Modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009–2010 Data Fusion Contest , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  T. Oke,et al.  Local Climate Zones for Urban Temperature Studies , 2012 .

[43]  John P. Kerekes,et al.  The IEEE GRSS standardized remote sensing data website: A step towards "science 2.0" in remote sensing , 2016 .

[44]  Gabriele Moser,et al.  Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest—Part B: 3-D Contest , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[45]  Yasushi Yamaguchi,et al.  Mineralogical mapping of southern Namibia by application of continuum-removal MSAM method to the HyMap data , 2013 .

[46]  Steffen Fritz,et al.  Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology , 2016, Remote. Sens..

[47]  Annekatrin Metz,et al.  EARTH OBSERVATION-SUPPORTED SERVICE PLATFORM FOR THE DEVELOPMENT AND PROVISION OF THEMATIC INFORMATION ON THE BUILT ENVIRONMENT – THE TEP-URBAN PROJECT , 2016 .

[48]  Lorenzo Bruzzone,et al.  Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances , 2016, IEEE Geoscience and Remote Sensing Magazine.

[49]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[50]  Carl F. Salk,et al.  Comparing the Quality of Crowdsourced Data Contributed by Expert and Non-Experts , 2013, PloS one.

[51]  Yee Leung,et al.  A co-training approach to the classification of local climate zones with multi-source data , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[52]  Benjamin Bechtel,et al.  Classification of Local Climate Zones Based on Multiple Earth Observation Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[53]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[54]  Johannes R. Sveinsson,et al.  Classification of hyperspectral data from urban areas based on extended morphological profiles , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[55]  Peijun Du,et al.  Spectral–Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[56]  Qian Du,et al.  Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[57]  T. Esch,et al.  Monitoring urbanization in mega cities from space , 2012 .