Landuse and land cover identification and disaggregating socio-economic data with convolutional neural network

Abstract We demonstrated an innovative learning method of convolutional neural network (CNN) to identify landuse and land cover (LULC) patterns and extract features to disaggregate socio-economic factors by using remote sensing imageries at 30 m spatial resolution. The training labels were extracted from the historical LULC map to reduce the huge cost of labelling work, and to provide an inaccuracy but sufficient training dataset. The fully connected layer of the trained CNN was extracted as disaggregating features to map socio-economic factors of population and gross domestic product (GDP). Results indicate that current method can attain 92% overall agreement of LULC identification with the cross-validation of other products. The determination coefficient of disaggregating socio-economic factors can reach 0.945 for population density, and 0.876 for GDP density with the cross-validation at county level.

[1]  Philip M. Fearnside,et al.  Global Warming and Tropical Land-Use Change: Greenhouse Gas Emissions from Biomass Burning, Decomposition and Soils in Forest Conversion, Shifting Cultivation and Secondary Vegetation , 2000 .

[2]  Liu Jiyuan,et al.  The land use and land cover change database and its relative studies in China , 2002 .

[3]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[4]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[5]  Yongyang Xu,et al.  Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters , 2018, Remote. Sens..

[6]  Sang Michael Xie,et al.  Combining satellite imagery and machine learning to predict poverty , 2016, Science.

[7]  I MohdHasmadi,et al.  Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data , 2009 .

[8]  Andrew K. Skidmore,et al.  Land use and land cover , 2002 .

[9]  Alan H. Strahler,et al.  Global land cover mapping from MODIS: algorithms and early results , 2002 .

[10]  Shattri Mansor,et al.  Land Cover Mapping Using Remote Sensing Data , 2020 .

[11]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[12]  Catherine Linard,et al.  Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data , 2015, PloS one.

[13]  Carlo Gatta,et al.  Unsupervised Deep Feature Extraction for Remote Sensing Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[15]  Intae Yang,et al.  Exploring Landsat 8 , 2015 .

[16]  E. Kalnay,et al.  Impact of urbanization and land-use change on climate , 2003, Nature.

[17]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[18]  Tieniu Tan,et al.  A Light CNN for Deep Face Representation With Noisy Labels , 2015, IEEE Transactions on Information Forensics and Security.

[19]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[20]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[21]  Onisimo Mutanga,et al.  Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers , 2014 .

[22]  John S. Gulliver,et al.  Dasymetric modelling of small-area population distribution using land cover and light emissions data , 2007 .

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

[24]  Richard Hans Robert Hahnloser,et al.  Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit , 2000, Nature.

[25]  Kenneth Grogan,et al.  A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring , 2016, Remote. Sens..

[26]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[27]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[28]  Abdullah F Rahman,et al.  The first global-scale 30 m resolution mangrove canopy height map using Shuttle Radar Topography Mission data , 2017 .

[29]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[30]  F. J. Gallego,et al.  Disaggregating population density of the European Union with CORINE land cover , 2011, Int. J. Geogr. Inf. Sci..

[31]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[32]  C. Elvidge,et al.  Nighttime Lights Compositing Using the VIIRS Day-Night Band: Preliminary Results , 2013 .

[33]  Yao Xin,et al.  Identification and analysis of secondary geological hazards triggered by a magnitude 8.0 Wenchuan Earthquake , 2009 .

[34]  Jun Chen,et al.  The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results , 2015, Remote. Sens..

[35]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[36]  ImageNet Classification with Deep Convolutional Neural , 2013 .

[37]  Gang Wang,et al.  Deep Learning-Based Classification of Hyperspectral Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.