Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones

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

[2]  Qiong Cao,et al.  Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data , 2017, Remote. Sens..

[3]  Xiuping Jia,et al.  Simplified Conditional Random Fields With Class Boundary Constraint for Spectral-Spatial Based Remote Sensing Image Classification , 2012, IEEE Geoscience and Remote Sensing Letters.

[4]  Cheolhee Yoo,et al.  Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[5]  Iryna Dronova,et al.  Urban Landscape Change Analysis Using Local Climate Zones and Object-Based Classification in the Salt Lake Metro Region, Utah, USA , 2019, Remote. Sens..

[6]  Liangpei Zhang,et al.  A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[7]  J. Quan,et al.  Multi-Temporal Effects of Urban Forms and Functions on Urban Heat Islands Based on Local Climate Zone Classification , 2019, International journal of environmental research and public health.

[8]  C. Jordan Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[9]  Soe W. Myint,et al.  Assessing local climate zones in arid cities: The case of Phoenix, Arizona and Las Vegas, Nevada , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[10]  Benjamin Bechtel,et al.  WUDAPT, an efficient land use producing data tool for mesoscale models? Integration of urban LCZ in WRF over Madrid , 2016 .

[11]  Xiao Xiang Zhu,et al.  EFFECT OF THE TRAINING SET CONFIGURATION ON SENTINEL-2-BASED URBAN LOCAL CLIMATE ZONE CLASSIFICATION , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[12]  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..

[13]  Fei Gao,et al.  Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF , 2019, Remote. Sens..

[14]  Jan Geletič,et al.  GIS-based delineation of local climate zones: The case of medium-sized Central European cities , 2016 .

[15]  Peerapon Vateekul,et al.  Road Segmentation of Remotely-Sensed Images Using Deep Convolutional Neural Networks with Landscape Metrics and Conditional Random Fields , 2017, Remote. Sens..

[16]  Benjamin Bechtel,et al.  Mapping Europe into local climate zones , 2019, PloS one.

[17]  Hanqiu Xu,et al.  Analysis of Impervious Surface and its Impact on Urban Heat Environment using the Normalized Difference Impervious Surface Index (NDISI) , 2010 .

[18]  Gerald Mills,et al.  Using LCZ data to run an urban energy balance model , 2015 .

[19]  W. Zhan,et al.  SUHI analysis using Local Climate Zones—A comparison of 50 cities , 2019, Urban Climate.

[20]  H. J. Scudder,et al.  Probability of error of some adaptive pattern-recognition machines , 1965, IEEE Trans. Inf. Theory.

[21]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[22]  Yueyan Liu,et al.  Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks , 2018, Remote. Sens..

[23]  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.

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

[25]  Xia Li,et al.  Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data , 2019, Remote. Sens..

[26]  Ming Yu,et al.  Spatial-Spectral Fusion Based on Conditional Random Fields for the Fine Classification of Crops in UAV-Borne Hyperspectral Remote Sensing Imagery , 2019, Remote. Sens..

[27]  M. Borodovsky,et al.  GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. , 2001, Nucleic acids research.

[28]  J. Unger,et al.  Design of an urban monitoring network based on Local Climate Zone mapping and temperature pattern modelling , 2014 .

[29]  Lifei Wei,et al.  Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery , 2019, Remote. Sens..

[30]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[31]  C. Ren,et al.  Mapping the local climate zones of urban areas by GIS-based and WUDAPT methods: A case study of Hong Kong , 2017, Urban Climate.

[32]  C. Ren,et al.  Investigating the influence of urban land use and landscape pattern on PM2.5 spatial variation using mobile monitoring and WUDAPT , 2019, Landscape and Urban Planning.

[33]  Xiao Xiang Zhu,et al.  Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network , 2019, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[34]  Xiao Xiang Zhu,et al.  Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets , 2018, Remote. Sens..

[35]  Nikos Fazakis,et al.  Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme , 2019, Entropy.

[36]  No-Wook Park,et al.  Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps , 2017, Remote. Sens..

[37]  Yangyang Li,et al.  Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels , 2019, Remote Sensing.

[38]  L. See,et al.  Climate modelling: Community initiative tackles urban heat. , 2015, Nature.

[39]  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.

[40]  Peijun Du,et al.  Multisource Earth Observation Data for Land-Cover Classification Using Random Forest , 2018, IEEE Geoscience and Remote Sensing Letters.

[41]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[42]  Q. Mcnemar Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

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

[44]  Edward Ng,et al.  GIS-based mapping of Local Climate Zone in the high-density city of Hong Kong , 2017, Urban Climate.

[45]  Ye Zhang,et al.  Hyperspectral Image Classification Based on Semi-Supervised Rotation Forest , 2017, Remote. Sens..

[46]  Catherine Linard,et al.  Using Local Climate Zones in Sub-Saharan Africa to tackle urban health issues , 2019, Urban Climate.