2,296
Views
1
CrossRef citations to date
0
Altmetric
Research Article

LuoJiaAI: A cloud-based artificial intelligence platform for remote sensing image interpretation

ORCID Icon, ORCID Icon, , , , & show all
Pages 218-241 | Received 05 Aug 2022, Accepted 21 Dec 2022, Published online: 23 Feb 2023

References

  • Abadi, M., A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, and M. Devin. 2016. “Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.” arXiv preprint arXiv:1603.04467.
  • Alzubaidi, L., J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie, and L. Farhan. 2021. “Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions.” Journal of Big Data 8 (1): 1–74. doi:10.1186/s40537-021-00444-8.
  • Amani, M., A. Ghorbanian, S. A. Ahmadi, M. Kakooei, A. Moghimi, S. M. Mirmazloumi, S. H. A. Moghaddam, S. Mahdavi, M. Ghahremanloo, and S. Parsian. 2020. “Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 5326–5350. doi:10.1109/JSTARS.2020.3021052.
  • Anwer, R. M., F. S. Khan, J. Van De Weijer, M. Molinier, and J. Laaksonen. 2018. “Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 138: 74–85. doi:10.1016/j.isprsjprs.2018.01.023.
  • Badgley, G., C. B. Field, and J. A. Berry. 2017. “Canopy Near-Infrared Reflectance and Terrestrial Photosynthesis.” Science Advances 3 (3): e1602244. doi:10.1126/sciadv.1602244.
  • Bisong, E. 2019. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Berlin, Germany: Springer.
  • Ceccherini, G., G. Duveiller, G. Grassi, G. Lemoine, V. Avitabile, R. Pilli, and A. Cescatti. 2020. “Abrupt Increase in Harvested Forest Area Over Europe After 2015.” Nature 583 (7814): 72–77. doi:10.1038/s41586-020-2438-y.
  • Chen, L. 2021. Deep Learning and Practice with MindSpore. Berlin, Germany: Springer Nature.
  • Chen, H., O. Engkvist, Y. Wang, M. Olivecrona, and T. Blaschke. 2018. “The Rise of Deep Learning in Drug Discovery.” Drug Discovery Today 23 (6): 1241–1250. doi:10.1016/j.drudis.2018.01.039.
  • Chen, T., T. Moreau, Z. Jiang, H. Shen, E. Q. Yan, L. Wang, Y. Hu, L. Ceze, C. Guestrin, and A. Krishnamurthy. 2018. “TVM: End-To-End Optimization Stack for Deep Learning.” arXiv preprint arXiv:1802.04799 11 (2018): 20.
  • Chen, L. C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2017. “Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected Crfs.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (4): 834–848. doi:10.1109/TPAMI.2017.2699184.
  • Chen, L. C., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018. “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 801–818.
  • Cheng, G., J. Han, and X. Lu. 2017. “Remote Sensing Image Scene Classification: Benchmark and State of the Art.” Proceedings of the IEEE 105 (10): 1865–1883. doi:10.1109/JPROC.2017.2675998.
  • Cheng, G., C. Yang, X. Yao, L. Guo, and J. Han. 2018. “When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs.” IEEE Transactions on Geoscience and Remote Sensing 56 (5): 2811–2821. doi:10.1109/TGRS.2017.2783902.
  • Dai, D., and W. Yang. 2010. “Satellite Image Classification via Two-Layer Sparse Coding with Biased Image Representation.” IEEE Geoscience and Remote Sensing Letters 8 (1): 173–176. doi:10.1109/LGRS.2010.2055033.
  • Daudt, R. C., B. Le Saux, A. Boulch, and Y. Gousseau. 2019. “Multitask Learning for Large-Scale Semantic Change Detection.” Computer Vision and Image Understanding 187: 102783. doi:10.1016/j.cviu.2019.07.003.
  • Deren, L. I., M. Wang, X. Shen, and Z. Dong. 2017. “From Earth Observation Satellite to Earth Observation Brain.” Geomatics & Information Ence of Wuhan University 42 (2): 143–149.
  • Ding, J., N. Xue, G. S. Xia, X. Bai, W. Yang, M. Yang, S. Belongie, J. Luo, M. Datcu, and M. Pelillo. 2021. “Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges.” IEEE Transactions on Pattern Analysis and Machine Intelligence 44: 7778–7796. doi:10.1109/TPAMI.2021.3117983.
  • Gao, S., J. Rao, Y. Kang, Y. Liang, J. Kruse, D. Doepfer, A. K. Sethi, J. F. M. Reyes, J. Patz, and B. S. Yandell. 2020. “Mobile Phone Location Data Reveal the Effect and Geographic Variation of Social Distancing on the Spread of the COVID-19 Epidemic.” arXiv preprint arXiv:2004.11430.
  • Gong, J. 2018. “Chances and Challenges for Development of Surveying and Remote Sensing in the Age of Artificial Intelligence.” Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University 43 (12): 1788–1796.
  • Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. doi:10.1016/j.rse.2017.06.031.
  • Han, J., J. Ding, J. Li, and G. S. Xia. 2021. “Align Deep Features for Oriented Object Detection.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–11. doi:10.1109/TGRS.2021.3062048.
  • Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, and T. R. Loveland. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (6160): 850–853. doi:10.1126/science.1244693.
  • He, K., G. Gkioxari, P. Dollár, and R. Girshick. 2017. “Mask R-CNN.” Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 2961–2969.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. “Deep Residual Learning for Image Recognition.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, 770–778.
  • Huawei Technologies Co., Ltd. 2022. “Huawei MindSpore AI Development Framework.” Artificial Intelligence Technology, 137–162. Springer.
  • Ji, S., S. Wei, and M. Lu. 2018. “Fully Convolutional Networks for Multisource Building Extraction from an Open Aerial and Satellite Imagery Data Set.” IEEE Transactions on Geoscience and Remote Sensing 57 (1): 574–586. doi:10.1109/TGRS.2018.2858817.
  • Kontgis, C., M. S. Warren, S. W. Skillman, R. Chartrand, and D. I. Moody. 2017. “Leveraging Sentinel-1 Time-Series Data for Mapping Agricultural Land Cover and Land Use in the Tropics.” 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), IEEE. Brugge, Belgium, 1–4.
  • Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. “Imagenet Classification with Deep Convolutional Neural Networks.” Communications of the ACM 60 (6): 84–90. doi:10.1145/3065386.
  • Kumar, L., and O. Mutanga. 2018. “Google Earth Engine Applications Since Inception: Usage, Trends, and Potential.” Remote Sensing 10 (10): 1509. doi:10.3390/rs10101509.
  • LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. doi:10.1038/nature14539.
  • Lewis, A., S. Oliver, L. Lymburner, B. Evans, L. Wyborn, N. Mueller, G. Raevksi, J. Hooke, R. Woodcock, and J. Sixsmith. 2017. “The Australian Geoscience Data Cube—foundations and Lessons Learned.” Remote Sensing of Environment 202: 276–292. doi:10.1016/j.rse.2017.03.015.
  • Li, S., S. He, S. Jiang, W. Jiang, and L. Zhang. 2022. “WHU-Stereo: A Challenging Benchmark for Stereo Matching of High-Resolution Satellite Images.” arXiv preprint arXiv:2206.02342.
  • Li, W., and C. Y. Hsu. 2020. “Automated Terrain Feature Identification from Remote Sensing Imagery: A Deep Learning Approach.” International Journal of Geographical Information Science 34 (4): 637–660. doi:10.1080/13658816.2018.1542697.
  • Li, J., X. Huang, and J. Gong. 2019. “Deep Neural Network for Remote-Sensing Image Interpretation: Status and Perspectives.” National Science Review 6 (6): 1082–1086. doi:10.1093/nsr/nwz058.
  • Li, K., G. Wan, G. Cheng, L. Meng, and J. Han. 2020. “Object Detection in Optical Remote Sensing Images: A Survey and a New Benchmark.” ISPRS Journal of Photogrammetry and Remote Sensing 159: 296–307. doi:10.1016/j.isprsjprs.2019.11.023.
  • Liermann, V. 2021. “Overview Machine Learning and Deep Learning Frameworks.” In The Digital Journey of Banking and Insurance, Volume III, 187–224. Cham, Switzerland: Springer.
  • Liu, J., and S. Ji. 2020. “A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-View Stereo Reconstruction from an Open Aerial Dataset.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 6050–6059.
  • Liu, Y., C. Pang, Z. Zhan, X. Zhang, and X. Yang. 2020. “Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model.” IEEE Geoscience and Remote Sensing Letters 18 (5): 811–815. doi:10.1109/LGRS.2020.2988032.
  • Liu, J., W. Wang, and H. Zhong. 2020. “EarthDataminer: A Cloud-Based Big Earth Data Intelligence Analysis Platform.” IOP Conference Series: Earth and Environmental Science 509 (1): 012032 (15pp). doi:10.1088/1755-1315/509/1/012032.
  • Long, J., E. Shelhamer, and T. Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hynes Convention Center in Boston, Massachusetts, USA, 3431–3440.
  • Ma, L., Y. Liu, X. Zhang, Y. Ye, G. Yin, and B. A. Johnson. 2019. “Deep Learning in Remote Sensing Applications: A Meta-Analysis and Review.” ISPRS Journal of Photogrammetry and Remote Sensing 152: 166–177. doi:10.1016/j.isprsjprs.2019.04.015.
  • Ma, Y., D. Yu, T. Wu, and H. Wang. 2019. “PaddlePaddle: An Open-Source Deep Learning Platform from Industrial Practice.” Frontiers of Data and Domputing 1 (1): 105–115.
  • MacDonald, A. J., and E. A. Mordecai. 2019. “Amazon Deforestation Drives Malaria Transmission, and Malaria Burden Reduces Forest Clearing.” Proceedings of the National Academy of Sciences 116 (44): 22212–22218. doi:10.1073/pnas.1905315116.
  • Maggiori, E., Y. Tarabalka, G. Charpiat, and P. Alliez. 2017. “Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark.” 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE. Fort Worth, TX, USA, 3226–3229.
  • Marc, B., K. Foster, G. Christie, S. Wang, G. D. Hager, and M. Brown. 2019. “Semantic Stereo for Incidental Satellite Images.” 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE. Waikoloa, HI, USA, 1524–1532.
  • Mutanga, O., and L. Kumar. 2019. Google Earth Engine Applications. MDPI.
  • Nemani, R. 2011. “NASA Earth Exchange: Next Generation Earth Science Collaborative.” ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 3820: 17–17. doi:10.5194/isprsarchives-XXXVIII-8-W20-17-2011.
  • Nemani, R. R., B. L. Thrasher, W. Wang, T. J. Lee, F. S. Melton, J. L. Dungan, and A. Michaelis. 2015. “NASA Earth Exchange (Nex) Supporting Analyses for National Climate Assessments.” AGU Fall Meeting Abstracts.
  • Ni, J., J. Wu, J. Tong, Z. Chen, and J. Zhao. 2020. “GC-Net: Global Context Network for Medical Image Segmentation.” Computer Methods and Programs in Biomedicine 190: 105121. doi:10.1016/j.cmpb.2019.105121.
  • Ojala, T., M. Pietikäinen, and D. Harwood. 1996. “A Comparative Study of Texture Measures with Classification Based on Featured Distributions.” Pattern Recognition 29 (1): 51–59. doi:10.1016/0031-3203(95)00067-4.
  • Paszke, A., S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, and L. Antiga. 2019. Pytorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems, edited by H. Wallach, Vol. 32, 8024–8035.
  • Pekel, J. F., A. Cottam, N. Gorelick, and A. S. Belward. 2016. “High-Resolution Mapping of Global Surface Water and Its Long-Term Changes.” Nature 540 (7633): 418–422. doi:10.1038/nature20584.
  • Rao, Q., and J. Frtunikj. 2018. “Deep Learning for Self-Driving Cars: Chances and Challenges.” Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems, Gothenburg, Sweden, 35–38.
  • Razakarivony, S., and F. Jurie. 2016. “Vehicle Detection in Aerial Imagery: A Small Target Detection Benchmark.” Journal of Visual Communication and Image Representation 34: 187–203. doi:10.1016/j.jvcir.2015.11.002.
  • Reichstein, M., G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, and N. Carvalhais. 2019. “Deep Learning and Process Understanding for Data-Driven Earth System Science.” Nature 566 (7743): 195–204. doi:10.1038/s41586-019-0912-1.
  • Ren, P., Y. Xiao, X. Chang, P. Y. Huang, Z. Li, X. Chen, and X. Wang. 2021. “A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions.” ACM Computing Surveys (CSUR) 54 (4): 1–34. doi:10.1145/3447582.
  • Ronneberger, O., P. Fischer, and T. Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham. Munich, Germany, 234–241.
  • Rottensteiner, F., G. Sohn, J. Jung, M. Gerke, C. Baillard, S. Benitez, and U. Breitkopf. 2012. “The ISPRS Benchmark on Urban Object Classification and 3D Building Reconstruction.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3 (2012), Nr. 1 1 (1): 293–298. doi:10.5194/isprsannals-I-3-293-2012.
  • Russakovsky, O., J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, and M. Bernstein. 2015. “Imagenet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision 115 (3): 211–252. doi:10.1007/s11263-015-0816-y.
  • Simonyan, K., and A. Zisserman. 2014. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” arXiv preprint arXiv:1409.1556.
  • Sumbul, G., M. Charfuelan, B. Demir, and V. Markl. 2019. “Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding.” IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE. Yokohama, Japan, 5901–5904.
  • Sun, Z., L. Sandoval, R. Crystal-Ornelas, S. M. Mousavi, J. Wang, C. Lin, N. Cristea, D. Tong, W. H. Carande, and X. Ma. 2022. “A Review of Earth Artificial Intelligence.” Computers & Geosciences 105034. doi:10.1016/j.cageo.2022.105034.
  • Sun, K., B. Xiao, D. Liu, and J. Wang. 2019. “Deep High-Resolution Representation Learning for Human Pose Estimation.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 5693–5703.
  • Sun, K., Y. Zhao, B. Jiang, T. Cheng, B. Xiao, D. Liu, Y. Mu, X. Wang, W. Liu, and J. Wang. 2019. “High-Resolution Representations for Labeling Pixels and Regions.” arXiv preprint arXiv:1904.04514.
  • Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. “Going Deeper with Convolutions.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA, 1–9.
  • Tamiminia, H., B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli, and B. Brisco. 2020. “Google Earth Engine for Geo-Big Data Applications: A Meta-Analysis and Systematic Review.” ISPRS Journal of Photogrammetry and Remote Sensing 164: 152–170. doi:10.1016/j.isprsjprs.2020.04.001.
  • Tenopir, C., N. M. Rice, S. Allard, L. Baird, J. Borycz, L. Christian, B. Grant, R. Olendorf, and R. J. Sandusky. 2020. “Data Sharing, Management, Use, and Reuse: Practices and Perceptions of Scientists Worldwide.” PLoS One 15 (3): e0229003. doi:10.1371/journal.pone.0229003.
  • Tong, X. Y., G. S. Xia, Q. Lu, H. Shen, S. Li, S. You, and L. Zhang. 2020. “Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models.” Remote Sensing of Environment 237: 111322. doi:10.1016/j.rse.2019.111322.
  • Toth, C., and G. Jóźków. 2016. “Remote Sensing Platforms and Sensors: A Survey.” ISPRS Journal of Photogrammetry and Remote Sensing 115: 22–36. doi:10.1016/j.isprsjprs.2015.10.004.
  • Van Etten, A., D. Lindenbaum, and T. M. Bacastow. 2018. “Spacenet: A Remote Sensing Dataset and Challenge Series.” arXiv preprint arXiv:1807.01232.
  • Wang, L., C. Diao, G. Xian, D. Yin, Y. Lu, S. Zou, and T. A. Erickson. 2020. “A Summary of the Special Issue on Remote Sensing of Land Change Science with Google Earth Engine.” Remote Sensing of Environment 248: 112002. Elsevier. doi:10.1016/j.rse.2020.112002.
  • Xia, G. S., X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang. 2018. “DOTA: A Large-Scale Dataset for Object Detection in Aerial Images.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, 3974–3983.
  • Xia, G. S., J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, and X. Lu. 2017. “AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification.” IEEE Transactions on Geoscience and Remote Sensing 55 (7): 3965–3981. doi:10.1109/TGRS.2017.2685945.
  • Xia, G. S., W. Yang, J. Delon, Y. Gousseau, H. Sun, and H. Maître. 2010. “Structural High-Resolution Satellite Image Indexing.” ISPRS TC VII Symposium-100 Years ISPRS, Vienna, Austria, 38, 298–303.
  • Xiong, J., P. S. Thenkabail, M. K. Gumma, P. Teluguntla, J. Poehnelt, R. G. Congalton, K. Yadav, and D. Thau. 2017. “Automated Cropland Mapping of Continental Africa Using Google Earth Engine Cloud Computing.” ISPRS Journal of Photogrammetry and Remote Sensing 126: 225–244. doi:10.1016/j.isprsjprs.2017.01.019.
  • Yang, C., Q. Huang, Z. Li, K. Liu, and F. Hu. 2017. “Big Data and Cloud Computing: Innovation Opportunities and Challenges.” International Journal of Digital Earth 10 (1): 13–53. doi:10.1080/17538947.2016.1239771.
  • Yao, Y., Z. Luo, S. Li, T. Fang, and L. Quan. 2018. “Mvsnet: Depth Inference for Unstructured Multi-View Stereo.” Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 767–783.
  • Young, T., D. Hazarika, S. Poria, and E. Cambria. 2018. “Recent Trends in Deep Learning Based Natural Language Processing.” IEEE Computational Intelligence Magazine 13 (3): 55–75. doi:10.1109/MCI.2018.2840738.
  • Yuan, Q., H. Shen, T. Li, Z. Li, S. Li, Y. Jiang, H. Xu, W. Tan, Q. Yang, and J. Wang. 2020. “Deep Learning in Environmental Remote Sensing: Achievements and Challenges.” Remote Sensing of Environment 241: 111716. doi:10.1016/j.rse.2020.111716.
  • Yue, P., B. Shangguan, L. Hu, L. Jiang, C. Zhang, Z. Cao, and Y. Pan. 2022. “Towards a Training Data Model for Artificial Intelligence in Earth Observation.” International Journal of Geographical Information Science 36: 1–25. doi:10.1080/13658816.2022.2087223.
  • Zhang, C., P. Yue, D. Tapete, L. Jiang, B. Shangguan, L. Huang, and G. Liu. 2020. “A Deeply Supervised Image Fusion Network for Change Detection in High Resolution Bi-Temporal Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 166: 183–200. doi:10.1016/j.isprsjprs.2020.06.003.
  • Zheng, Z., Y. Zhong, A. Ma, and L. Zhang. 2020. “FPGA: Fast Patch-Free Global Learning Framework for Fully End-To-End Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 58 (8): 5612–5626. doi:10.1109/TGRS.2020.2967821.
  • Zhong, Y., X. Hu, C. Luo, X. Wang, J. Zhao, and L. Zhang. 2020. “WHU-Hi: UAV-Borne Hyperspectral with High Spatial Resolution (H2) Benchmark Datasets and Classifier for Precise Crop Identification Based on Deep Convolutional Neural Network with CRF.” Remote Sensing of Environment 250: 112012. doi:10.1016/j.rse.2020.112012.
  • Zhu, X. X., D. Tuia, L. Mou, G. S. Xia, L. Zhang, F. Xu, and F. Fraundorfer. 2017. “Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources.” IEEE Geoscience and Remote Sensing Magazine 5 (4): 8–36. doi:10.1109/MGRS.2017.2762307.