1 |
Sameen, M. I. and B. Pradhan, 2016b. A Simplified Semi-Automatic Technique for Highway Extraction from High-Resolution Airborne LiDAR Data and Orthophotos, Journal of the Indian Society of Remote Sensing, 45(3): 1-11
|
2 |
Sameen, M. I. and B. Pradhan, 2017. Severity Prediction of Traffic Accidents with Recurrent Neural Networks, Applied Sciences, 7(6): 476
|
3 |
Sameen, M. I. and B. Pradhan, 2017a. Severity Prediction of Traffic Accidents with Recurrent Neural Networks, Applied Sciences, 7(6): 476
DOI
|
4 |
Sameen, M. I. and B. Pradhan, 2017b. A Two-Stage Optimization Strategy for Fuzzy Object-Based Analysis Using Airborne LiDAR and High- Resolution Orthophotos for Urban Road Extraction, Journal of Sensors.
|
5 |
Saxe, A., P.W. Koh, Z. Chen, M. Bhand, B. Suresh, and A.Y Ng, 2011. On random weights and unsupervised feature learning, Proc. of the 28th international conference on machine learning (ICML-11), Bellevue, WA, Jun. 28-Jul. 2, pp. 1089-1096
|
6 |
Shi, W., Z. Miao, and J. Debayle, 2014. An integrated method for urban main-road centerline extraction from optical remotely sensed imagery, IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3359-3372
DOI
|
7 |
Sohail, M., D.A.C. Maunder, and S. Cavill, 2006. Effective regulation for sustainable public transport in developing countries, Transport Policy, 13(3): 177-190
DOI
|
8 |
Unsalan, C. and B. Sirmacek, 2012. Road network detection using probabilistic and graph theoretical methods, IEEE Transactions on Geoscience and Remote Sensing, 50(11): 4441-4453
DOI
|
9 |
Wang, J., J. Song, M. Chen, and Z. Yang, 2015. Road network extraction: A neural-dynamic framework based on deep learning and a finite state machine, International Journal of Remote Sensing, 36(12): 3144-3169
DOI
|
10 |
Vitor, G.B., D.A. Lima, A.C. Victorino, and J.V. Ferreira, 2013. A 2D/3D vision based approach applied to road detection in urban environments, Proc. of 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast City, Australia, Jun. 23-26, pp. 952-957.
|
11 |
Wegner, J. D., J.A. Montoya-Zegarra, and K. Schindler, 2013. A higher-order CRF model for road network extraction, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, Jun. 23-28, pp. 1698-1705
|
12 |
Raschka, S., 2015. Python machine learning, Packt Publishing Ltd., U.K
|
13 |
Majumder, N., S. Poria, A. Gelbukh, and E. Cambria, 2017. Deep Learning-Based Document Modeling for Personality Detection from Text, IEEE Intelligent Systems, 32(2): 74-79
DOI
|
14 |
Mnih, V. and G.E. Hinton. 2010. Learning to detect roads in high-resolution aerial images, Proc. of 11th European Conference on Computer Vision, Grete, Greece, Sep. 5-11, pp. 210-223
|
15 |
Mockus, J., 1975. On Bayesian methods for seeking the extremum, Proc. of Optimization Techniques IFIP Technical Conference Novosibirsk, Berlin Heidelberg, Germany, Jul. 1-7, pp. 400-404
|
16 |
Nareyek, A., 2003. Choosing search heuristics by nonstationary reinforcement learning, Applied Optimization, 86: 523-544
DOI
|
17 |
Osborne, B. P., V. J. Osborne, and M. L. Kruger, 2012. Comparison of satellite surveying to traditional surveying methods for the resources industry, Journal of the British Interplanetary Society, 65(2): 98-104
|
18 |
Rathore, M. M., A. Ahmad, A. Paul, and S. Rho, 2016. Urban planning and building smart cities based on the internet of things using big data analytics, Computer Networks, 101: 63-80
DOI
|
19 |
Saito, S., T. Yamashita, and Y. Aoki, 2016. Multiple object extraction from aerial imagery with convolutional neural networks, Electronic Imaging, 2016(10): 1-9
|
20 |
Sameen, M. I. and B. Pradhan, 2016a. Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS, Geomatics, Natural Hazards and Risk, 1-15
|
21 |
Devkota, K. C., A. D. Regmi, H. R. Pourghasemi, K. Yoshida, B. Pradhan, I. C. Ryu, M. R. Dhital, and O.F. Althuwaynee, 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling- Narayanghat road section in Nepal Himalaya, Natural hazards, 65(1): 135-165
DOI
|
22 |
Bergstra, J. and Y. Bengio, 2012. Random search for hyper-parameter optimization, Journal of Machine Learning Research, 13: 281-305
|
23 |
Colak, S., A. Lima, and M.C. Gonzalez, 2016. Understanding congested travel in urban areas, Nature communications, 7
|
24 |
Dahl, G. E., D. Yu, L. Deng, and A. Acero, 2012. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on audio, speech, and language processing, 20(1): 30-42
DOI
|
25 |
Kumar, P., C. P. McElhinney, P. Lewis, and T. McCarthy, 2014. Automated road markings extraction from mobile laser scanning data, International Journal of Applied Earth Observation and Geoinformation, 32: 125-137
DOI
|
26 |
Kaddah, W., Y. Ouerhani, A. Alfalou, M. Desthieux, C. Brosseau, and C. Gutierrez, 2016. Roadmarking features extraction using theVIAPIX(R) system, Optics Communications,371: 117-127
DOI
|
27 |
Krizhevsky, A., I. Sutskever, and G.E. Hinton, 2012. Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, 1097-1105
|
28 |
Kumar, P., 2012. Road features extraction using terrestrial mobile laser scanning system, National University of Ireland Maynooth, Ireland.
|
29 |
Kussul, N., M. Lavreniuk, S. Skakun, and A. Shelestov, 2017. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data, IEEE Geoscience and Remote Sensing Letters, 14(5): 778-782
DOI
|
30 |
Latinopoulos, D. and K. Kechagia, 2015. A GIS-based multi-criteria evaluation for wind farm site selection, A regional scale application in Greece. Renewable Energy, 78: 550-560
DOI
|
31 |
LeCun, Y., Y. Bengio, and G. Hinton, 2015. Deep learning, Nature, 521(7553): 436-444
DOI
|
32 |
Li, M., A. Stein, W. Bijker, and Q. Zhan, 2016. Regionbased urban road extraction from VHR satellite images using binary partition tree, International Journal of Applied Earth Observation and Geoinformation, 44: 217-225
DOI
|
33 |
Jones, D. R., 2001. A taxonomy of global optimization methods based on response surfaces, Journal of global optimization, 21(4): 345-383
DOI
|
34 |
Zeyer, A., P. Doetsch, P. Voigtlaender, R. Schlüter, and H. Ney, 2017. A comprehensive study of deep bidirectional LSTM RNNs for acoustic modeling in speech recognition, Proc. of Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference, New Orleans, LA, Mar. 5-9, pp. 2462-2466
|
35 |
Weihs, C., K. Luebke, and I. Czogiel, 2006. Response surface methodology for optimizing hyper parameters, Universität Dortmund, Dortmund, Germany
|
36 |
Xie, X., K.B.Y. Wong, H. Aghajan, P. Veelaert, and W. Philips, 2016. Road network inference through multiple track alignment, Transportation Research Part C: Emerging Technologies, 72: 93-108
DOI
|
37 |
Yang, B., L. Fang, Q. Li, and J. Li, 2012. Automated extraction of road markings from mobile LiDAR point clouds, Photogrammetric Engineering & Remote Sensing, 78(4): 331-338
DOI
|
38 |
Zhan, C., 1993. A hybrid line thinning approach, Proc. of Autocarto-conference-, ASPRS American society for photogrammetry and remote sensing, Bethesda, MD, p. 396
|