How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

BigData andmachine learning (ML) technologies have the potential to impactmany facets of environment andwatermanagement (EWM). BigData are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and socialmedia have contributed to the proliferation of BigData inmany EWMfields, such asweather forecasting, disastermanagement, smart water and energymanagement systems, and remote sensing. BigData brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics.ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn fromdata.MLmay help unlock the power of BigData if properly integratedwith data analytics. Recent breakthroughs inAI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features fromdata, with better predictive performance and less human intervention. Collectively BigData andML techniques have shown great potential for data-driven decisionmaking, scientific discovery, and process optimization. These technological advancesmay greatly benefit EWM, especially because (1)many EWMapplications (e.g. early floodwarning) require the capability to extract useful information from a large amount of data in autonomousmanner and in real time, (2)EWMresearches have become highlymultidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge aboutmany EWMprocesses is still incomplete, but whichmay now be complemented through data-driven discovery. A large number of applications onBigData andML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in BigData andML, (3) provide a systematic review of current applications, andfinally (4) discussmajor issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM,with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWMresearch.

[1]  F. Gao,et al.  Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data , 2014 .

[2]  Petr Sojka,et al.  Gensim -- Statistical Semantics in Python , 2011 .

[3]  Baojun Zhao,et al.  Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Trac D. Tran,et al.  Hyperspectral Image Classification Using Dictionary-Based Sparse Representation , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[5]  R. Reedy,et al.  Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data , 2018, Proceedings of the National Academy of Sciences.

[6]  Xiaolin Zhu,et al.  Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions , 2018, Remote. Sens..

[7]  Sanja Fidler,et al.  Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Junwei Han,et al.  A Survey on Object Detection in Optical Remote Sensing Images , 2016, ArXiv.

[9]  Mirela G. Tulbure,et al.  Modeling 25 years of spatio-temporal surface water and inundation dynamics on large river basin scale using time series of Earth observation data , 2016 .

[10]  Xiuping Jia,et al.  Deep Fusion of Remote Sensing Data for Accurate Classification , 2017, IEEE Geoscience and Remote Sensing Letters.

[11]  Alexander Y. Sun,et al.  Discovering State‐Parameter Mappings in Subsurface Models Using Generative Adversarial Networks , 2018, Geophysical Research Letters.

[12]  Junwei Han,et al.  Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Manzhu Yu,et al.  Big Data in Natural Disaster Management: A Review , 2018 .

[14]  Norman Kerle,et al.  UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning , 2014 .

[15]  Xiangyun Hu,et al.  Translation-aware semantic segmentation via conditional least-square generative adversarial networks , 2017 .

[16]  Francesca Cigna,et al.  The relationship between intermittent coherence and precision of ISBAS InSAR ground motion velocities: ERS-1/2 case studies in the UK , 2017 .

[17]  Reynold Xin,et al.  Apache Spark , 2016 .

[18]  Ping Zhong,et al.  A Hybrid DBN and CRF Model for Spectral-Spatial Classification of Hyperspectral Images , 2017 .

[19]  SchmidhuberJürgen.  Deep learning in neural networks , 2015 .

[20]  Kun Zhu,et al.  Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Ahmed H. Elsheikh,et al.  Parametrization and generation of geological models with generative adversarial networks , 2017, 1708.01810.

[22]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[23]  GandomiAmir,et al.  Beyond the hype , 2015 .

[24]  John E. Hopcroft,et al.  Stacked Generative Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[26]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

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

[28]  Katharina Schleidt,et al.  Extending INSPIRE to the Internet of Things through SensorThings API , 2018 .

[29]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[30]  David Schneider.  U.S. supercomputing strikes back , 2018, IEEE Spectrum.

[31]  Ye Zhang,et al.  A light and faster regional convolutional neural network for object detection in optical remote sensing images , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[32]  Ping Tan,et al.  DualGAN: Unsupervised Dual Learning for Image-to-Image Translation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[33]  Charles E. Catlett,et al.  Array of things: a scientific research instrument in the public way: platform design and early lessons learned , 2017, SCOPE@CPSWeek.

[34]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Luis Samaniego,et al.  Scaling, similarity, and the fourth paradigm for hydrology , 2017 .

[36]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[37]  Simon Elias Bibri,et al.  The core enabling technologies of big data analytics and context-aware computing for smart sustainable cities: a review and synthesis , 2017, Journal of Big Data.

[38]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Stefano Nativi,et al.  Big Data challenges in building the Global Earth Observation System of Systems , 2015, Environ. Model. Softw..

[40]  Martina Slámová,et al.  The application of civic technologies in a field survey of landslides , 2018 .

[41]  Shuyuan Yang,et al.  Pan-sharpening via deep metric learning , 2018 .

[42]  Lin Zhu,et al.  Hyperspectral Images Classification With Gabor Filtering and Convolutional Neural Network , 2017, IEEE Geoscience and Remote Sensing Letters.

[43]  D. Dransch,et al.  Volunteered Geographic Information for Disaster Management with Application to Rapid Flood Damage Estimation , 2019 .

[44]  Dursun Delen,et al.  Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud , 2013, Decis. Support Syst..

[45]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[46]  Michael Luca,et al.  Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life , 2015 .

[47]  Patrick Lambert,et al.  3-D Deep Learning Approach for Remote Sensing Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[48]  Jon Atli Benediktsson,et al.  Big Data for Remote Sensing: Challenges and Opportunities , 2016, Proceedings of the IEEE.

[49]  Lajiao Chen,et al.  Recent advance in earth observation big data for hydrology , 2018 .

[50]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[51]  Jichun Wu,et al.  Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media , 2018, Water Resources Research.

[52]  Patrick Meier,et al.  Digital Humanitarians: How Big Data Is Changing the Face of Humanitarian Response , 2015 .

[53]  Shen-Chuan Tai,et al.  Pansharpening by interspectral similarity and edge information using improved deep residual network , 2018, J. Electronic Imaging.

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

[55]  Derek T. Anderson,et al.  Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community , 2017 .

[56]  Delf Rothe,et al.  Seeing like a satellite: Remote sensing and the ontological politics of environmental security , 2017 .

[57]  Peter J. Diggle,et al.  Statistical Analysis of Spatial and Spatio-Temporal Point Patterns , 2013 .

[58]  Hylke van der Schaaf,et al.  Mapping the OGC SensorThings API onto the OpenIoT Middleware , 2014, OpenIoT@SoftCOM.

[59]  Alexander Y. Sun,et al.  Model Calibration and Parameter Estimation: For Environmental and Water Resource Systems , 2015 .

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

[61]  Jon Atli Benediktsson,et al.  Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[62]  Carol J. Friedland,et al.  A SURVEY OF UNMANNED AERIAL VEHICLE ( UAV ) USAGE FOR IMAGERY , 2011 .

[63]  Shanlin Yang,et al.  Big data driven smart energy management: From big data to big insights , 2016 .

[64]  Yaokui Cui,et al.  Global water cycle and remote sensing big data: overview, challenge, and opportunities , 2018, Big Earth Data.

[65]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[66]  Qingshan Liu,et al.  Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[67]  Jiang Li,et al.  Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..

[68]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[69]  Peng Yue,et al.  A Sensor Web and Web Service-Based Approach for Active Hydrological Disaster Monitoring , 2016, ISPRS Int. J. Geo Inf..

[70]  Rudy Arthur,et al.  Social sensing of floods in the UK , 2017, PloS one.

[71]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[72]  João Porto de Albuquerque,et al.  Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring , 2018, Comput. Geosci..

[73]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[74]  Gang Fu,et al.  Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network , 2017, Remote. Sens..

[75]  S. Jonkman.  Global Perspectives on Loss of Human Life Caused by Floods , 2005 .

[76]  Awais Ahmad,et al.  Advanced computing model for geosocial media using big data analytics , 2017, Multimedia Tools and Applications.

[77]  Yu Liu,et al.  Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery , 2017, Remote. Sens..

[78]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[79]  Max Ritts,et al.  Smart Earth: A meta-review and implications for environmental governance , 2018, Global Environmental Change.

[80]  Maggi Kelly,et al.  A review of the emergent ecosystem of collaborative geospatial tools for addressing environmental challenges , 2017, Comput. Environ. Urban Syst..

[81]  Fei Xie,et al.  Research on spatial and temporal characteristics of drought based on GIS using Remote Sensing Big Data , 2016, Cluster Computing.

[82]  Wen Zhang,et al.  Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR , 2017, ISPRS Int. J. Geo Inf..

[83]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[84]  Shashi Shekhar,et al.  The Nexus of Food, Energy, and Water Resources: Visions and Challenges in Spatial Computing , 2017 .

[85]  Zhenlong Li,et al.  Big Data and cloud computing: innovation opportunities and challenges , 2017, Int. J. Digit. Earth.

[86]  Ming-Yu Liu,et al.  Coupled Generative Adversarial Networks , 2016, NIPS.

[87]  Derek Anderson,et al.  Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community , 2017 .

[88]  P. Beran,et al.  Reduced-order modeling: new approaches for computational physics , 2004 .

[89]  M. N. Giriprasad,et al.  SAR images denoising using a novel stochastic diffusion wavelet scheme , 2018, Cluster Computing.

[90]  Christoph Aubrecht,et al.  Speeding up the clock in remote sensing: identifying the ‘black spots’ in exposure dynamics by capitalizing on the full spectrum of joint high spatial and temporal resolution , 2017, Natural Hazards.

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

[92]  Guangluan Xu,et al.  Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks , 2018, Remote. Sens..

[93]  Albert Y. Zomaya,et al.  Remote sensing big data computing: Challenges and opportunities , 2015, Future Gener. Comput. Syst..

[94]  Lianru Gao,et al.  Semantic Labeling of High Resolution Aerial Imagery and LiDAR Data with Fine Segmentation Network , 2018, Remote. Sens..

[95]  Michael N Fienen,et al.  Metamodels to Bridge the Gap Between Modeling and Decision Support. , 2015, Ground water.

[96]  Davide Cozzolino,et al.  Pansharpening by Convolutional Neural Networks , 2016, Remote. Sens..

[97]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[98]  D. Lobell,et al.  Satellite-based assessment of yield variation and its determinants in smallholder African systems , 2017, Proceedings of the National Academy of Sciences.

[99]  Luke S. Smith,et al.  Assessing the utility of social media as a data source for flood risk management using a real‐time modelling framework , 2017 .

[100]  Farhad Samadzadegan,et al.  Spectral–spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder , 2017 .

[101]  Yehia El-khatib,et al.  Web technologies for environmental Big Data , 2015, Environ. Model. Softw..

[102]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[103]  Zhanyi Hu,et al.  Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks , 2017 .

[104]  Varun Chandola,et al.  Machine learning for energy-water nexus: challenges and opportunities , 2018, Big Earth Data.

[105]  V. Singh,et al.  A review of drought concepts , 2010 .

[106]  Ivana Podnar Žarko,et al.  Interoperability and Open-Source Solutions for the Internet of Things , 2015, Lecture Notes in Computer Science.

[107]  Lorenzo Bruzzone,et al.  A Deep Network Architecture for Super-Resolution-Aided Hyperspectral Image Classification With Classwise Loss , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[108]  Stefano Nativi,et al.  Reviewing innovative Earth observation solutions for filling science-policy gaps in hydrology , 2014 .

[109]  Nicholas Zabaras,et al.  Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification , 2018, J. Comput. Phys..

[110]  Luis Samaniego,et al.  Scaling, Similarity, and the Fourth Paradigm for Hydrology , 2017, Hydrology and earth system sciences.

[111]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[112]  Liangpei Zhang,et al.  Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach , 2017, 1707.03558.

[113]  Amir Hossein Alavi,et al.  Machine learning in geosciences and remote sensing , 2016 .

[114]  Geli Zhang,et al.  Divergent trends of open-surface water body area in the contiguous United States from 1984 to 2016 , 2018, Proceedings of the National Academy of Sciences.

[115]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.

[116]  Rodney Anthony Stewart,et al.  Web-based knowledge management system: linking smart metering to the future of urban water planning , 2010 .

[117]  Paul Zikopoulos,et al.  Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data , 2011 .

[118]  Sven J. Dickinson,et al.  Integrating Region and Boundary Information for Improved Spatial Coherencein Object Tracking , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[119]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[120]  Raul Morais,et al.  Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..

[121]  Jean-Raynald de Dreuzy,et al.  Prospective Interest of Deep Learning for Hydrological Inference , 2017, Ground water.

[122]  Jeffrey Heer,et al.  Termite: visualization techniques for assessing textual topic models , 2012, AVI.

[123]  João Paulo Ramos Teixeira,et al.  Remote sensing of drought: Progress, challenges and opportunities , 2015 .

[124]  Awais Ahmad,et al.  Urban planning and building smart cities based on the Internet of Things using Big Data analytics , 2016, Comput. Networks.

[125]  Christian Heipke,et al.  Information from imagery: ISPRS scientific vision and research agenda , 2016 .

[126]  Adnan Darwiche,et al.  Human-level intelligence or animal-like abilities? , 2017, Commun. ACM.

[127]  Cyrill Stachniss,et al.  WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming , 2018, Remote. Sens..

[128]  David Walling,et al.  Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch? , 2019, Water Resources Research.

[129]  Zhiguo Jiang,et al.  Airport Detection Using End-to-End Convolutional Neural Network with Hard Example Mining , 2017, Remote. Sens..

[130]  Alexander Y. Sun,et al.  A learning-based data-driven forecast approach for predicting future reservoir performance , 2018 .

[131]  Alfred Stein,et al.  Recurrent Multiresolution Convolutional Networks for VHR Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[132]  Eric Laloy,et al.  Training‐Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network , 2017, ArXiv.

[133]  M. Goodchild,et al.  Data-driven geography , 2014, GeoJournal.

[134]  Yang Yang,et al.  Multi-Temporal Remote Sensing Image Registration Using Deep Convolutional Features , 2018, IEEE Access.

[135]  Kim-Kwang Raymond Choo,et al.  Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification , 2016, Soft Computing.

[136]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[137]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[138]  Michael Kampffmeyer,et al.  Urban Land Cover Classification With Missing Data Modalities Using Deep Convolutional Neural Networks , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[139]  D. Lobell,et al.  Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries , 2017 .

[140]  Prabhat,et al.  Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets , 2016, ArXiv.

[141]  Bertrand Le Saux,et al.  Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images , 2017, Remote. Sens..

[142]  Jianbo Liu,et al.  Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks , 2018, ISPRS Int. J. Geo Inf..

[143]  Kuolin Hsu,et al.  HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community , 2018, Hydrology and Earth System Sciences.

[144]  Anthony J. Jakeman,et al.  A review of surrogate models and their application to groundwater modeling , 2015 .

[145]  U. Wehn,et al.  The social innovation potential of ICT-enabled citizen observatories to increase eParticipation in local flood risk management , 2015 .

[146]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[147]  Tom Spencer,et al.  Big Data Approaches for coastal flood risk assessment and emergency response , 2018, WIREs Climate Change.

[148]  WangLizhe,et al.  Remote sensing big data computing , 2015 .

[149]  Xiao Yang,et al.  Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network , 2017, 1707.06611.

[150]  Jef Caers,et al.  Direct forecasting of subsurface flow response from non-linear dynamic data by linear least-squares in canonical functional principal component space , 2014 .

[151]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[152]  Shunichi Koshimura,et al.  Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami , 2018, Remote. Sens..

[153]  Bertrand Le Saux,et al.  Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

[154]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[155]  Rajiv Ranjan,et al.  Towards building a data-intensive index for big data computing - A case study of Remote Sensing data processing , 2015, Inf. Sci..

[156]  Dale L. Zimmerman.  13 – Statistical Analysis of Spatial Data , 1994 .

[157]  Roberta E. Martin,et al.  Storm-triggered landslides in the Peruvian Andes and implications for topography, carbon cycles, and biodiversity , 2016 .

[158]  Yiwen Wang,et al.  Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification , 2017, Remote. Sens..

[159]  Fabiana Calò,et al.  Potentiality and Limitations of Open Satellite Data for Flood Mapping , 2018 .

[160]  Zhong Chen,et al.  End-to-End Airplane Detection Using Transfer Learning in Remote Sensing Images , 2018, Remote. Sens..

[161]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[162]  Peter Reinartz,et al.  Exploring the Potential of Conditional Adversarial Networks for Optical and SAR Image Matching , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[163]  Z. Irani,et al.  Critical analysis of Big Data challenges and analytical methods , 2017 .

[164]  Meng Wang,et al.  An On-Demand Retrieval Method Based on Hybrid NoSQL for Multi-Layer Image Tiles in Disaster Reduction Visualization , 2017, ISPRS Int. J. Geo Inf..

[165]  Dorothee Spuhler,et al.  The Potential of Knowing More: A Review of Data-Driven Urban Water Management. , 2017, Environmental science & technology.

[166]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[167]  Lin Zhu,et al.  Generative Adversarial Networks for Hyperspectral Image Classification , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[168]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

[169]  Dawei Han,et al.  Big data and hydroinformatics , 2016 .

[170]  Brenden Jongman,et al.  Assessing flood risk at the global scale: model setup, results, and sensitivity , 2013 .

[171]  Guoqing Li,et al.  MapReduce functions to remote sensing distributed data processing—Global vegetation drought monitoring as example , 2018, Softw. Pract. Exp..

[172]  Wei Lee Woon,et al.  Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks , 2017 .

[173]  David Denkenberger,et al.  Classification of global catastrophic risks connected with artificial intelligence , 2018, AI & SOCIETY.

[174]  Bo Yu,et al.  A practical trial of landslide detection from single-temporal Landsat8 images using contour-based proposals and random forest: a case study of national Nepal , 2018, Landslides.

[175]  Yang Hong,et al.  Exploring Deep Neural Networks to Retrieve Rain and Snow in High Latitudes Using Multisensor and Reanalysis Data , 2018, Water Resources Research.

[176]  Tony Hey,et al.  The Fourth Paradigm: Data-Intensive Scientific Discovery , 2009 .

[177]  Shiqiang Zhang,et al.  Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review , 2018, Reviews of Geophysics.

[178]  Jing Shuai,et al.  Topic modelling of ecology, environment and poverty nexus: An integrated framework , 2018, Agriculture, Ecosystems & Environment.

[179]  Mélanie Frappier,et al.  The Book of Why: The New Science of Cause and Effect , 2018, Science.

[180]  Liping Di,et al.  Deriving High Spatiotemporal Remote Sensing Images Using Deep Convolutional Network , 2018, Remote. Sens..

[181]  Antonio-Javier Gallego,et al.  Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks , 2018, Remote. Sens..

[182]  Liangpei Zhang,et al.  Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network , 2017, IEEE Geoscience and Remote Sensing Letters.

[183]  Alan C. Bovik,et al.  Surface Water Mapping by Deep Learning , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[184]  Dan Hu,et al.  Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks , 2018, IEEE Geoscience and Remote Sensing Letters.

[185]  David Saurí,et al.  Household Smart Water Metering in Spain: Insights from the Experience of Remote Meter Reading in Alicante , 2017 .

[186]  Guido Luzi,et al.  Ground-based synthetic aperture radar interferometry for deformation monitoring: a case study at Geheyan Dam, China , 2017 .

[187]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[188]  Dhabaleswar K. Panda,et al.  Characterization of Big Data Stream Processing Pipeline: A Case Study using Flink and Kafka , 2017, BDCAT.

[189]  Wei Liu,et al.  Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake , 2016, Remote. Sens..

[190]  Philip S. Beran,et al.  Reduced-order modeling - New approaches for computational physics , 2001 .

[191]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[192]  Guangmin Sun,et al.  Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images , 2017 .

[193]  Patrick Martin,et al.  The Six Pillars for Building Big Data Analytics Ecosystems , 2016, ACM Comput. Surv..

[194]  Maoguo Gong,et al.  Feature learning and change feature classification based on deep learning for ternary change detection in SAR images , 2017 .

[195]  Mirela G. Tulbure,et al.  Modeling multidecadal surface water inundation dynamics and key drivers on large river basin scale using multiple time series of Earth‐observation and river flow data , 2017 .

[196]  Yunhao Liu,et al.  Big Data: A Survey , 2014, Mob. Networks Appl..

[197]  K. P. Sudheer,et al.  Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions , 2010, Environ. Model. Softw..

[198]  M. Hilbert,et al.  Big Data for Development: A Review of Promises and Challenges , 2016 .

[199]  Nagiza F. Samatova,et al.  Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data , 2016, IEEE Transactions on Knowledge and Data Engineering.

[200]  Ying Wang,et al.  Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images , 2017, Remote. Sens..

[201]  Louis J. Durlofsky,et al.  A New Data-Space Inversion Procedure for Efficient Uncertainty Quantification in Subsurface Flow Problems , 2017, Mathematical Geosciences.

[202]  Alexander Y. Sun,et al.  Metamodeling-based approach for risk assessment and cost estimation: Application to geological carbon sequestration planning , 2018, Comput. Geosci..

[203]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[204]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[205]  Huiming Tang,et al.  Pixel-wise regression using U-Net and its application on pansharpening , 2018, Neurocomputing.

[206]  Xiaojin Zhu,et al.  Incorporating domain knowledge into topic modeling via Dirichlet Forest priors , 2009, ICML '09.

[207]  J. Blumenstock.  Don’t forget people in the use of big data for development , 2018, Nature.

[208]  A. Budden,et al.  Big data and the future of ecology , 2013 .

[209]  Chao Zeng,et al.  Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[210]  Marta Ortiz-de-Urbina-Criado,et al.  A model for the analysis of data-driven innovation and value generation in smart cities' ecosystems , 2017 .

[211]  Janet Wiles,et al.  Use of an automatic content analysis tool: A technique for seeing both local and global scope , 2009, Int. J. Hum. Comput. Stud..