Acharya TD, Lee DH (2019) Landslide susceptibility mapping using relative frequency and predictor rate along Araniko Highway. KSCE J Civ Eng 23(2):763–776
Acheampong AO, Boateng EB (2019) Modelling carbon emission intensity: Application of artificial neural network. J Clean Prod 225:833–856
Akgun A, Erkan O (2016) Landslide susceptibility mapping by geographical information system-based multivariate statistical and deterministic models: In an artificial reservoir area at Northern Turkey. Arab J Geosci 9(2):165
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bulletin of engineering geology the environment 58(1):21–44
Ali SA, Parvin F, Pham QB, Vojtek M, Vojtekova J, Costache R,.. . Ghorbani MA (2020) GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia. Ecol Ind 117:106620
Allaby A, Allaby M (1991) Concise Oxford dictionary of earth sciences. Oxford University Press
Arabameri A, Pradhan B, Rezaei K, Lee S, Sohrabi M (2020) An ensemble model for landslide susceptibility mapping in a forested area. Geocarto International 35(15):1680–1705
Arabameri A, Saha S, Roy J, Chen W, Blaschke T, Bui T, D (2020) Landslide susceptibility evaluation and management using different machine learning methods in the Gallicash River Watershed, Iran. Remote Sensing 12(3):475
Azizi A, Malekmohammadi B, Jafari HR, Nasiri H, Parsa VA (2014) Land suitability assessment for wind power plant site selection using ANP-DEMATEL in a GIS environment: case study of Ardabil province, Iran. Environ Monit Assess 186(10):6695–6709
Bahrami Y, Hassani H, Maghsoudi A (2020) Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran. GeoJournal, 1–20
Bai S-B, Wang J, Lü G-N, Zhou P-G, Hou S-S, Xu S-N (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115(1–2):23–31
Bandara K, Bergmeir C, Smyl S (2020) Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach. Expert Syst Appl 140:112896
Berberian M (1981) Active faulting and tectonics of Iran. Zagros Hindu Kush Himalaya Geodynamic Evolution 3:33–69
Berberian M (1983) Generalized tectonic map of Iran. Continental Deformation in the Iranian Plateau: Contribution to the Seismotectonics of Iran, part IV, Geol. Surv. Iran, 52
Betts H, Basher L, Dymond J, Herzig A, Marden M, Phillips C (2017) Development of a landslide component for a sediment budget model. Environ Model Softw 92:28–39
Bragagnolo L, da Silva R, Grzybowski J (2020) Artificial neural network ensembles applied to the mapping of landslide susceptibility. Catena 184:104240
Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5(6):853–862
Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378
Can A, Dagdelenler G, Ercanoglu M, Sonmez H (2019) Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms. Bulletin of engineering geology the environment 78(1):89–102
Can A, Dagdelenler G, Ercanoglu M, Sonmez H (2019) Landslide susceptibility mapping at Ovacık-Karabük (Turkey) using different artificial neural network models: comparison of training algorithms. Bulletin of engineering geology the environment 78(1):89–102
Cascini L, Bonnard C, Corominas J, Jibson R, Montero-Olarte J (2005) Landslide hazard and risk zoning for urban planning and development. Landslide Risk Management. Taylor and Francis, London, 199–235
Conforti M, Pascale S, Robustelli G, Sdao F (2014) Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena 113:236–250
Corominas J, van Westen C, Frattini P, Cascini L, Malet J-P, Fotopoulou S,.. . Agliardi F (2014) Recommendations for the quantitative analysis of landslide risk. Bulletin of engineering geology the environment 73(2):209–263
Cortez B, Carrera B, Kim Y-J, Jung J-Y (2018) An architecture for emergency event prediction using LSTM recurrent neural networks. Expert Syst Appl 97:315–324
Costanzo D, Rotigliano E, Irigaray Fernández C, Jiménez-Perálvarez JD, Chacón Montero J (2012) Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain)
Cracknell MJ, Reading AM (2014) Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Computers geosciences 63:22–33
Dai F, Lee C, Li J, Xu Z (2001) Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong. Environ Geol 40(3):381–391
Das I, Stein A, Kerle N, Dadhwal VK (2012) Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology 179:116–125
De Blasio FV (2011) Introduction to the physics of landslides: lecture notes on the dynamics of mass wasting. Springer Science & Business Media
De Smith MJ, Goodchild MF, Longley P (2007) Geospatial analysis: a comprehensive guide to principles, techniques and software tools. Troubador publishing ltd
Ding A, Zhang Q, Zhou X, Dai B (2016) Automatic recognition of landslide based on CNN and texture change detection. Paper presented at the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC)
Dodangeh E, Choubin B, Eigdir AN, Nabipour N, Panahi M, Shamshirband S, Mosavi A (2020) Integrated machine learning methods with resampling algorithms for flood susceptibility prediction. Sci Total Environ 705:135983
Fang W, Ding Y, Zhang F, Sheng VS (2019) DOG: A new background removal for object recognition from images. Neurocomputing 361:85–91
Fang Z, Wang Y, Peng L, Hong H (2020) Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Computers geosciences 139:104470
Farrokhnia A, Pirasteh S, Pradhan B, Pourkermani M, Arian M (2011) A recent scenario of mass wasting and its impact on the transportation in Alborz Mountains, Iran using geo-information technology. Arab J Geosci 4(7–8):1337–1349
Feizizadeh B, Blaschke T, Nazmfar H (2014) GIS-based ordered weighted averaging and Dempster–Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin, Iran. Int J Digit Earth 7(8):688–708
Feizizadeh B, Blaschke T, Nazmfar H, Rezaei Moghaddam M (2013) Landslide susceptibility mapping for the Urmia Lake basin, Iran: a multi-criteria evaluation approach using GIS. International Journal of Environmental Research 7(2):319–336
Feizizadeh B, Jankowski P, Blaschke T (2014) A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis. Computers geosciences 64:81–95
Florinsky IV (1998) Accuracy of local topographic variables derived from digital elevation models. Int J Geogr Inf Sci 12(1):47–62
Garrett J (1994) Where and why artificial neural networks are applicable in civil engineering
Ghorbanzadeh O, Blaschke T, Aryal J, Gholaminia K (2020) A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. Journal of Spatial Science 65(3):401–418
Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J (2019) Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing 11(2):196
Girshick R (2015) Fast r-cnn. Paper presented at the Proceedings of the IEEE international conference on computer vision
Grana D, Della Rossa E (2010) Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion. Geophysics 75(3):O21–O37
Graves A (2013) Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850
Graves A, Liwicki M, Fernández S, Bertolami R, Bunke H, Schmidhuber J (2008) A novel connectionist system for unconstrained handwriting recognition. IEEE Trans Pattern Anal Mach Intell 31(5):855–868
Guirado E, Tabik S, Alcaraz-Segura D, Cabello J, Herrera F (2017) Deep-learning convolutional neural networks for scattered shrub detection with google earth imagery. arXiv preprint arXiv:1706.00917
Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1–4):181–216
Hadji R, Limani Y, Baghem M, Demdoum A (2013) Geologic, topographic and climatic controls in landslide hazard assessment using GIS modeling: a case study of Souk Ahras region, NE Algeria. Quatern Int 302:224–237
Haeri S, Satari M (1993) Great Landslides Triggered by Manjil Earthquake, 20 June 1990. Natural Disaster Reduction Center of Iran (In Persian)
Havenith H-B, Strom A, Torgoev I, Torgoev A, Lamair L, Ischuk A, Abdrakhmatov K (2015) Tien Shan geohazards database: Earthquakes and landslides. Geomorphology 249:16–31
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition
Henriques C, Zêzere JL, Marques F (2015) The role of the lithological setting on the landslide pattern and distribution. Engineering geology 189:17–31
Hirasawa T, Aoyama K, Tanimoto T, Ishihara S, Shichijo S, Ozawa T,.. . Fujisaki J (2018) Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21(4):653–660
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural computation 9(8):1735–1780
Hu Q, Zhou Y, Wang S, Wang F (2020) Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin. Geomorphology 351:106975
Jaafari A, Panahi M, Pham BT, Shahabi H, Bui DT, Rezaie F, Lee S (2019) Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena 175:430–445
Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Science 349(6245):255–260
Kalchbrenner N, Blunsom P (2013) Recurrent continuous translation models. Paper presented at the Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455–5516
Khan RU, Zhang X, Kumar R (2019) Analysis of ResNet and GoogleNet models for malware detection. Journal of Computer Virology Hacking Techniques 15(1):29–37
Krenker A, Bešter J, Kos A (2011) Introduction to the artificial neural networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications. InTech, 1–18
Krizhevsky A, Sutskever I, Hinton GE (2012) Advances in neural information processing systems. Neural Information Processing Systems Foundation, 1269
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90
Lacasse S, Nadim F (2009) Landslide risk assessment and mitigation strategy. In: Landslides–disaster risk reduction. Springer, pp 31–61
LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324
Lee S, Ryu J-H, Lee M-J, Won J-S (2006) The application of artificial neural networks to landslide susceptibility mapping at Janghung, Korea. Math Geol 38(2):199–220
Lee S, Ryu J-H, Won J-S, Park H-J (2004) Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering geology 71(3–4):289–302
Li Y, Chen W (2020) Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water 12(1):113
Lin C-W, Liu S-H, Lee S-Y, Liu C-C (2006) Impacts of the Chi-Chi earthquake on subsequent rainfall-induced landslides in central Taiwan. Engineering geology 86(2–3):87–101
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition
Lutgens FK, Tarbuck EJ, Tasa DG (2017) Essentials of geology. Pearson
Ma J, Ding Y, Gan VJ, Lin C, Wan Z (2019) Spatiotemporal prediction of PM2. 5 concentrations at different time granularities using IDW-BLSTM. IEEE Access 7:107897–107907
Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2016) Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans Geosci Remote Sens 55(2):645–657
Mehrabi M, Pradhan B, Moayedi H, Alamri A (2020) Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques. Sensors 20(6):1723
Mersha T, Meten M (2020) GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenvironmental Disasters 7(1):1–22
Mikolov T (2012) Statistical language models based on neural networks. Presentation at Google, Mountain View, 2nd April, 80, 26
Moayedi H, Mehrabi M, Mosallanezhad M, Rashid ASA, Pradhan B (2019) Modification of landslide susceptibility mapping using optimized PSO-ANN technique. Engineering with Computers 35(3):967–984
Moore ID, Grayson R, Ladson A (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological processes 5(1):3–30
Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. Paper presented at the ICML
Neaupane KM, Piantanakulchai M (2006) Analytic network process model for landslide hazard zonation. Engineering geology 85(3–4):281–294
Ngo PTT, Panahi M, Khosravi K, Ghorbanzadeh O, Karimnejad N, Cerda A, Lee S (2020) Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran. Geoscience Frontiers
Nsengiyumva JB, Luo G, Amanambu AC, Mind'je R, Habiyaremye G, Karamage F,.. . Mupenzi C (2019) Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. Sci Total Environ 659:1457–1472
Oh H-J, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers geosciences 37(9):1264–1276
Park HJ, Lee JH, Woo I (2013) Assessment of rainfall-induced shallow landslide susceptibility using a GIS-based probabilistic approach. Engineering geology 161:1–15
Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68(5):1443–1464
Pham BT, Jaafari A, Prakash I, Bui DT (2019) A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bulletin of engineering geology the environment 78(4):2865–2886
Pham BT, Phong TV, Nguyen HD, Qi C, Al-Ansari N, Amini A,.. . Ly H-B (2020) A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping. Water 12(1):239
Pham BT, Prakash I, Dou J, Singh SK, Trinh PT, Tran HT,.. . Shirzadi A (2020) A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto International 35(12):1267–1292
Pham BT, Bui T, Indra D, P., & Dholakia M (2015) Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS–based statistical approach of frequency ratio method. Int J Eng Res Technol 4(11):338–344
Pradhan B, Lee S (2010) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60(5):1037–1054
Qayyum A, Malik AS, Saad NM, Iqbal M, Faris Abdullah M, Rasheed W,.. . Bin Jafaar MY (2017) Scene classification for aerial images based on CNN using sparse coding technique. Int J Remote Sens 38(8–10):2662–2685
Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7(2):725–742
Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149
Salvati P, Bianchi C, Rossi M, Guzzetti F (2010) Societal landslide and flood risk in Italy. Natural Hazards & Earth System Sciences, 10(3)
Sameen MI, Sarkar R, Pradhan B, Drukpa D, Alamri AM, Park H-J (2020) Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests. Computers geosciences 134:104336
San BT (2014) An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: the Candir catchment area (western Antalya, Turkey). International journal of applied earth observation geoinformation 26:399–412
Sarkar S, Kanungo D (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering Remote Sensing 70(5):617–625
Schmidt J, Evans IS, Brinkmann J (2003) Comparison of polynomial models for land surface curvature calculation. Int J Geogr Inf Sci 17(8):797–814
Segall P, Pollard D (1980) Mechanics of discontinuous faults. Journal of Geophysical Research: Solid Earth 85(B8):4337–4350
Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition workshops
Shary PA (1995) Land surface in gravity points classification by a complete system of curvatures. Math Geol 27(3):373–390
Shimoda Y, Ochiai H (2006) Slide switch assemblies. In: Google Patents
Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I,.. . Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298
Sifa SF, Mahmud T, Tarin MA, Haque DME (2020) Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: A case study of Rangamati district in Bangladesh. Geology Ecology Landscapes 4(3):222–235
Sörensen R, Zinko U, Seibert J (2006) On the calculation of the topographic wetness index:. evaluation of different methods based on field observations
Sun D, Wen H, Wang D, Xu J (2020) A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology, 107201
Sze V, Chen Y-H, Yang T-J, Emer JS (2017) Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295–2329
Thanh DQ, Nguyen DH, Prakash I, Jaafari A, Nguyen V-T, Van Phong T, Pham BT (2020) GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam. Vietnam J Earth Sci 42:55–66
Thiery Y, Malet J-P, Sterlacchini S, Puissant A, Maquaire O (2007) Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology 92(1–2):38–59
Tsangaratos P, Ilia I, Hong H, Chen W, Xu C (2017) Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. Landslides 14(3):1091–1111
Villarrubia G, De Paz JF, Chamoso P, De la Prieta F (2018) Artificial neural networks used in optimization problems. Neurocomputing 272:10–16
Wang G, Lei X, Chen W, Shahabi H, Shirzadi A (2020) Hybrid computational intelligence methods for landslide susceptibility mapping. Symmetry 12(3):325
Wang S-C (2003) Artificial neural network. In: Interdisciplinary computing in java programming. Springer, pp 81–100
Wang Y, Fang Z, Hong H (2019) Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ 666:975–993
Wang Y, Fang Z, Wang M, Peng L, Hong H (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Computers geosciences 138:104445
Westen Cv, Terlien M (1996) An approach towards deterministic landslide hazard analysis in GIS. A case study from Manizales (Colombia). Earth surface processes landforms 21(9):853–868
Wilde M, Günther A, Reichenbach P, Malet J-P, Hervás J (2018) Pan-European landslide susceptibility mapping: ELSUS Version 2. Journal of maps 14(2):97–104
Wu Y, Ke Y, Chen Z, Liang S, Zhao H, Hong H (2020) Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping. Catena 187:104396
Xie J, Uchimura T, Chen P, Liu J, Xie C, Shen Q (2019) A relationship between displacement and tilting angle of the slope surface in shallow landslides. Landslides 16(6):1243–1251
Xie M, Esaki T, Zhou G (2004) GIS-based probabilistic mapping of landslide hazard using a three-dimensional deterministic model. Natural hazards 33(2):265–282
Yalcin A, Reis S, Aydinoglu A, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85(3):274–287
Yang HL, Lunga D, Yuan J (2017) Toward country scale building detection with convolutional neural network using aerial images. Paper presented at the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836
Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv preprint arXiv:1409.2329
Zeiler MD, Fergus R (2013) Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint arXiv:1301.3557
Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth surface processes landforms 12(1):47–56
Zhao X, Chen W (2020) GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques. Applied Sciences 10(1):16
Zhao Y, Wang R, Jiang Y, Liu H, Wei Z (2019) GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China. Engineering geology 259:105147
Zhu XX, Tuia D, Mou L, Xia G-S, Zhang L, Xu F, Fraundorfer F (2017) Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience Remote Sensing Magazine 5(4):8–36