Long-term annual mapping of four cities on different continents by applying a deep information learning method to landsat data
Open accessAbstract
Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectral bands over decades are most desirable for this purpose. In practice, however, temporal spectral variance arising from variations in atmospheric conditions, sensor calibration, cloud cover, and other factors complicates extraction of consistent information on changes in urban land cover. Moreover, the construction and application of effective training samples is time-consuming, especially at continental and global scales. Here, we propose a new framework for satellite-based mapping of urban areas based on transfer learning and deep learning techniques. We apply this method to Landsat observations collected during 1984–2016 and extract annual records of urban areas in four cities in the temperate zone (Beijing, New York, Melbourne, and Munich). The method is trained using observations of Beijing collected in 1999, and then used to map urban areas in all target cities for the entire 1984–2016 period. The method addresses two central challenges in long term detection of urban change: temporal spectral variance and a scarcity of training samples. First, we use a recurrent neural network to minimize seasonal urban spectral variance. Second, we introduce an automated transfer strategy to maximize information gain from limited training samples when applied to new target cities in similar climate zones. Compared with other state-of-the-art methods, our method achieved comparable or even better accuracy: the average change detection accuracy during 1984–2016 is 89% for Beijing, 94% for New York, 93% for Melbourne, and 89% for Munich, and the overall accuracy of single-year urban maps is approximately 96 ± 3% among the four target cities. The results demonstrate the practical potential and suitability of the proposed framework. The method is a promising tool for detecting urban change in massive remote sensing data sets with limited training data.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Lyu et al.'s approach integrated a transfer learning strategy, attaining the possibility to reuse training samples in different periods for urban LULC mapping and change analysis.
“Moreover, their approach integrated a transfer learning strategy, thus attaining the possibility to reuse the training samples in different periods for conducting urban LULC mapping and change analysis.”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Lyu et al., 2018
Lyu et al. (2018) used an RNN deep learning model to minimize seasonal urban spectral variance and ensured a high classification accuracy of approximately 96% for urban LULC maps of one year.
“From the perspective of practical application analysis, Lyu et al. (2018) recently used RNN DL model to minimize seasonal urban spectral variance, and consequently ensured a high classification accuracy of approximately 96% for urban LULC maps of one year.”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Lyu et al., 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Google Earth Engine: Planetary-scale geospatial analysis for everyone
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.06.031 · Cited by 15,523 · see citing works
Global Consequences of Land Use
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., et al. (2005) · Science · doi:10.1126/science.1111772 · Cited by 13,272 · see citing works
Global Change and the Ecology of Cities
Grimm, N., Faeth, S., Golubiewski, N., Redman, C., Wu, J., Bai, X.-M., & Briggs, J. (2008) · Science · doi:10.1126/science.1150195 · Cited by 6,789 · see citing works
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061 · see citing works
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390 · see citing works
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400 · see citing works
Self-citationDynamics of Land-Use and Land-Cover Change in Tropical Regions
Lambin, É. F., Geist, H., & Lepers, E. (2003) · Annual Review of Environment and Resources · doi:10.1146/annurev.energy.28.050302.105459 · Cited by 3,009 · see citing works
Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2_4 · Cited by 2,727 · see citing works
Object-based cloud and cloud shadow detection in Landsat imagery
Zhu, Z., & Woodcock, C. E. (2011) · Remote Sensing of Environment · doi:10.1016/j.rse.2011.10.028 · Cited by 2,252 · see citing works
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Lu, L., Niu, Z., Huang, X., Fu, H., Liu, S., et al. (2012) · International Journal of Remote Sensing · doi:10.1080/01431161.2012.748992 · Cited by 1,707 · see citing works
Self-citationCombining satellite imagery and machine learning to predict poverty
Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016) · Science · doi:10.1126/science.aaf7894 · Cited by 1,664 · see citing works
A Landsat Surface Reflectance Dataset for North America, 1990–2000
Masek, J. G., Vermote, E. F., Saleous, N., Wolfe, R. E., HALL, F. G., Huemmrich, K. F., Feng Gao, A., Kutler, J., & Lim, T. K. (2006) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2005.857030 · Cited by 1,628 · see citing works
Development of a 2001 National Land-Cover Database for the United States
Homer, C. G., Huang, C., Yang, L., Wylie, B. K., & Coan, M. J. (2004) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.70.7.829 · Cited by 1,567 · see citing works
Deep Recurrent Neural Networks for Hyperspectral Image Classification
Mou, L., Ghamisi, P., & Zhu, X. X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2636241 · Cited by 1,397 · see citing works
Self-citationSchneider, A., Friedl, M. A., & Potere, D. (2010) · Remote Sensing of Environment · doi:10.1016/j.rse.2010.03.003 · Cited by 727 · see citing works
Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks
Marmanis, D., Datcu, M. P., Esch, T., & Stilla, U. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2499239 · Cited by 652 · see citing works
Ma, T., Zhou, C., Pei, T., Haynie, S., & Fan, J. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.04.018 · Cited by 510 · see citing works
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
Xie, S. M., Jean, N., Burke, M., Lobell, D. B., & Ermon, S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9906 · Cited by 469 · see citing works
Yang, L., Huang, C., Homer, C. G., Wylie, B. K., & Coan, M. J. (2003) · Canadian Journal of Remote Sensing · doi:10.5589/m02-098 · Cited by 461 · see citing works
Schneider, A. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.06.006 · Cited by 446 · see citing works
Li, C., Wang, J., Wang, L., Hu, L., & Gong, P. (2014) · Remote Sensing · doi:10.3390/rs6020964 · Cited by 414 · see citing works
Self-citationA 30-year (1984–2013) record of annual urban dynamics of Beijing City derived from Landsat data
Li, X., Gong, P., & Lu, L. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.06.007 · Cited by 384 · see citing works
Self-citationLearning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection
Lyu, H., Lu, H. M., & Mou, L. (2016) · Remote Sensing · doi:10.3390/rs8060506 · Cited by 333 · see citing works
Self-citationRegional mapping of human settlements in southeastern China with multisensor remotely sensed data
Lu, D., Tian, H., Zhou, G., & Ge, H. (2008) · Remote Sensing of Environment · doi:10.1016/j.rse.2008.05.009 · Cited by 331 · see citing works
China’s urban expansion from 1990 to 2010 determined with satellite remote sensing
Wang, L., Li, C., Ying, Q., Cheng, X. Y., Wang, X., Li, X., Hu, L., Lu, L., Yu, L., Huang, H., et al. (2012) · Chinese Science Bulletin · doi:10.1007/s11434-012-5235-7 · Cited by 321 · see citing works
Self-citationA SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data
Cao, X., Chen, J., Imura, H., & Higashi, O. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2009.06.001 · Cited by 279 · see citing works
Change of impervious surface area between 2001 and 2006 in the conterminous United States
Xian, G., Homer, C. G., Dewitz, J. A., Fry, J. A., Hossain, N., & Wickham, J. (2011) · Photogrammetric Engineering & Remote Sensing · Cited by 258 · see citing works
Cohen, I., Cozman, F. G., Sebe, N., Cirelo, M. C., & Huang, T. S. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.127 · Cited by 251 · see citing works
Sexton, J., Song, X., Huang, C., Channan, S., Baker, M. E., & TOWNSHEND, J. R. G. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.10.025 · Cited by 250 · see citing works
Song, X., Sexton, J., Huang, C., Channan, S., & TOWNSHEND, J. R. G. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.12.027 · Cited by 249 · see citing works
Li, X., Li, W., Middel, A., Harlan, S. L., Brazel, A. J., & Turner, B. L. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.12.022 · Cited by 249 · see citing works
Self-citationExpansion and growth in Chinese cities, 1978–2010
Schneider, A., & Mertes, C. (2014) · Environmental Research Letters · doi:10.1088/1748-9326/9/2/024008 · Cited by 237 · see citing works
Gong, P., & Howarth, P. J. (1990) · Cited by 232 · see citing works
Self-citationThe first all-season sample set for mapping global land cover with Landsat-8 data
Li, C., Gong, P., Wang, J., Zhu, Z., Biging, G. S., Yuan, C., Hu, T., Zhang, H., Wang, Q., Li, X., et al. (2017) · 中国科学通报：英文版 · doi:10.1016/j.scib.2017.03.011 · Cited by 191 · see citing works
Self-citationDetecting change in urban areas at continental scales with MODIS data
Mertes, C., Schneider, A., Sulla‐Menashe, D., Tatem, A. J., & Tan, B. (2014) · Remote Sensing of Environment · doi:10.1016/j.rse.2014.09.023 · Cited by 179 · see citing works
Landsat digital enhancements for change detection in urban environments
Howarth, P. J., & Boasson, E. (1983) · Remote Sensing of Environment · doi:10.1016/0034-4257(83)90019-6 · Cited by 134 · see citing works
Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods
Yang, C., Wu, G., Ding, K., Shi, T., Li, Q., & Wang, J. (2017) · Remote Sensing · doi:10.3390/rs9121222 · Cited by 95 · see citing works
Self-citationBuilding a Better Urban Picture: Combining Day and Night Remote Sensing Imagery
Zhang, Q., Li, B., Thau, D., & Moore, R. (2015) · Remote Sensing · doi:10.3390/rs70911887 · Cited by 83 · see citing works
Feyisa, G. L., Meilby, H., Jenerette, G. D., & Pauleit, S. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.12.026 · Cited by 79 · see citing works
Mapping urban change in the UK using satellite radar interferometry
Grey, W. M. F., Luckman, A. J., & Holland, D. A. (2003) · Remote Sensing of Environment · doi:10.1016/s0034-4257(03)00142-1 · Cited by 77 · see citing works
Analysis of Multivariate Social Science Data (2nd ed.)
Rupp, A. A., & Sweet, S. J. (2011) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2011.607725 · Cited by 57 · see citing works
Integrating Gaussian mixtures into deep neural networks: Softmax layer with hidden variables
Tüske, Z., Tahir, M. A., Schlüter, R., & Ney, H. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178779 · Cited by 42 · see citing works
Shi, L., Ling, F., Ge, Y., Foody, G. M., Li, X., Wang, L., Zhang, Y., & Du, Y. (2017) · Remote Sensing · doi:10.3390/rs9111148 · Cited by 38 · see citing works
Self-citationCracknell, A. P., Kanniah, K. D., Tan, K. P., & Wang, L. (2013) · International Journal of Remote Sensing · doi:10.1080/01431161.2013.820367 · Cited by 34 · see citing works
A supervised Thematic Mapper classification with a purification of training samples
ARAI, K. (1992) · International Journal of Remote Sensing · doi:10.1080/01431169208904251 · Cited by 29 · see citing works
Cracknell, A. P., Kanniah, K. D., Tan, K. P., & Wang, L. (2015) · International Journal of Remote Sensing · doi:10.1080/01431161.2014.995278 · Cited by 24 · see citing works
Changing patterns of urban-rural nutrient flows in China: driving forces and options
Yu, C., Xiao, Y., & Ni, S. (2016) · 中国科学通报：英文版 · doi:10.1016/j.scib.2016.12.006 · Cited by 22 · see citing works
Shahtahmassebi, A. R., Lin, Y., Lin, L., Atkinson, P. M., Moore, N. J., Wang, K., He, S., Huang, L., Wu, J., Shen, Z., et al. (2017) · Remote Sensing · doi:10.3390/rs9070682 · Cited by 21 · see citing works
Self-citationSydney & Melbourne: A tale of two cities
McGilvray, A. (2016) · Nature · doi:10.1038/538s58a · Cited by 2 · see citing works
Flagged first, then by citations
4 of the 58 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Tieleman, T.; Hinton, G.G. Lecture 6.5-RmsProp: Divide the gradient by a running average of its recent magnitude. Neural Netw. Mach. Learn. 2012, 4, 26-31.
- Cutler, A.; Cutler, D.R.; Stevens, J.R. Random Forests. Mach. Learn. 2004, 45, 157-176.
- Tong, S.; Koller, D. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2001, 2, 45-66.
- Bartholomew, D.J.; Steele, F.; Galbraith, J.I.; Moustaki, I. Analysis of multivariate social science data. Struct. Equ. Model. Multidiscip. J. 2011, 18, 686-693.
Cited by
Sorted by citations
Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456 · see citing works
Deep learning based multi-temporal crop classification
Zhong, L., Hu, L., & Zhou, H. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.11.032 · Cited by 1,042 · see citing works
Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges
Shi, W., Zhang, M., Zhang, R., Chen, S., & Zhan, Z. (2020) · Remote Sensing · doi:10.3390/rs12101688 · Cited by 599 · see citing works
Gong, P., Li, X., & Wei, 张. Z. (2019) · 中国科学通报：英文版 · doi:10.1016/j.scib.2019.04.024 · Cited by 500 · see citing works
Self-citationA deep translation (GAN) based change detection network for optical and SAR remote sensing images
Li, X., Du, Z., Huang, Y., & Tan, Z. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.07.007 · Cited by 403 · see citing works
Self-citationHemati, M., Hasanlou, M., Mahdianpari, M., & Mohammadimanesh, F. (2021) · Remote Sensing · doi:10.3390/rs13152869 · Cited by 262 · see citing works
Mou, L., & Zhu, X. X. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2933609 · Cited by 234 · see citing works
Self-citationWagner, F. H., Sánchez, A., Tarabalka, Y., Lotte, R. G., Ferreira, M. P., Aidar, M. P. M., Gloor, E. U., Phillips, O. L., & Aragão, L. E. O. C. (2019) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.111 · Cited by 225 · see citing works
Zhang, D., Pan, Y., Zhang, J., Hu, T., Zhao, J., Li, N., & Chen, Q. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111912 · Cited by 167 · see citing works
Spatial Sequential Recurrent Neural Network for Hyperspectral Image Classification
Zhang, X., Sun, Y., Jiang, K., Li, C., Jiao, L., & Zhou, H. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2844873 · Cited by 166 · see citing works
A review of multi-class change detection for satellite remote sensing imagery
Zhu, Q., Guo, X., Li, Z., & Li, D. (2022) · Geo-spatial Information Science · doi:10.1080/10095020.2022.2128902 · Cited by 166 · see citing works
Mazzia, V., Khaliq, A., & Chiaberge, M. (2019) · Applied Sciences · doi:10.3390/app10010238 · Cited by 162 · see citing works
Change detection based on Faster R-CNN for high-resolution remote sensing images
Wang, Q., Zhang, X., Chen, G., Dai, F., Gong, Y., & Zhu, K. (2018) · Remote Sensing Letters · doi:10.1080/2150704x.2018.1492172 · Cited by 141 · see citing works
Analysis on change detection techniques for remote sensing applications: A review
Afaq, Y., & Manocha, A. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101310 · Cited by 130 · see citing works
Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks
Li, H., Ghamisi, P., Soergel, U., & Zhu, X. X. (2018) · Remote Sensing · doi:10.3390/rs10101649 · Cited by 98 · see citing works
Self-citationZhong, L., Hu, L., Zhou, H., & Tao, X. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111411 · Cited by 93 · see citing works
Wang, L.-K., Zhang, M., Gao, X., & Shi, W.-Z. (2024) · Remote Sensing · doi:10.3390/rs16050804 · Cited by 91 · see citing works
An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection
Liu, J., Xuan, W., Gan, Y., Zhan, Y., Liu, J., & Du, B. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108960 · Cited by 78 · see citing works
Mou, L., & Zhu, X. X. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.02091 · Cited by 70 · see citing works
Self-citationApplication of Deep Learning in Multitemporal Remote Sensing Image Classification
Cheng, X., Sun, Y., Zhang, W., Wang, Y., Cao, X., & Wang, Y. (2023) · Remote Sensing · doi:10.3390/rs15153859 · Cited by 67 · see citing works
Beyond Supervised Learning in Remote Sensing: A Systematic Review of Deep Learning Approaches
Hosseiny, B., Mahdianpari, M., Hemati, M., Radman, A., Mohammadimanesh, F., & Chanussot, J. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3316733 · Cited by 68 · see citing works
Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China
Yi, Z., Li, J., & Chen, Q. (2020) · Remote Sensing · doi:10.3390/rs12244052 · Cited by 63 · see citing works
Dou, P., Shen, H., Li, Z., & Guan, X. (2021) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2021.102477 · Cited by 63 · see citing works
A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications
Ghanbari, H., Mahdianpari, M., Homayouni, S., & Mohammadimanesh, F. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3065569 · Cited by 62 · see citing works
Deep-Learning for Change Detection Using Multi-Modal Fusion of Remote Sensing Images: A Review
Saidi, S., Idbraim, S., Karmoude, Y., Masse, A., & Arbelo, M. (2024) · Remote Sensing · doi:10.3390/rs16203852 · Cited by 57 · see citing works
Chang, S., Wang, Z., Mao, D., Guan, K., Jia, M., & Chen, C. (2020) · Remote Sensing · doi:10.3390/rs12152488 · Cited by 54 · see citing works
Selection of Landsat 8 OLI Band Combinations for Land Use and Land Cover Classification
Yu, Z., Di, L., Yang, R., Tang, J., Lin, L., Zhang, C., Rahman, M. S., Zhao, H., Gaigalas, J., Yu, G., et al. (2019) · International Conference on Agro-Geoinformatics · doi:10.1109/agro-geoinformatics.2019.8820595 · Cited by 53 · see citing works
Using An Attention-Based LSTM Encoder–Decoder Network for Near Real-Time Disturbance Detection
Yuan, Y., Lin, L., Huo, L., Kong, Y., Zhou, Z.-G., Wu, B., & Jia, Y. M. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.2988324 · Cited by 51 · see citing works
Xie, B., Zhang, H. K., & Xue, J. (2019) · Sensors · doi:10.3390/s19102398 · Cited by 50 · see citing works
NestNet: a multiscale convolutional neural network for remote sensing image change detection
Yu, X., Fan, J., Chen, J., Zhang, P., Zhou, Y., & Han, L. (2021) · International Journal of Remote Sensing · doi:10.1080/01431161.2021.1906982 · Cited by 48 · see citing works
Inferring 3D change detection from bitemporal optical images
Marsocci, V., Coletta, V., Ravanelli, R., Scardapane, S., & Crespi, M. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.009 · Cited by 41 · see citing works
Deep Learning From Multiple Crowds: A Case Study of Humanitarian Mapping
Chen, J., Zhou, Y., Zipf, A., & Fan, H. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2868748 · Cited by 36 · see citing works
A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data
Yu, J., Zeng, P., Yu, Y., Yu, H., Huang, L., & Zhou, D. (2022) · Remote Sensing · doi:10.3390/rs14051128 · Cited by 34 · see citing works
Impact of Urbanization on Regional Rainfall-Runoff Processes: Case Study in Jinan City, China
Zhao, Y., Xia, J., Xu, Z., Qiao, Y., Shen, J., & Ye, C. (2023) · Remote Sensing · doi:10.3390/rs15092383 · Cited by 30 · see citing works
Satellite-based change detection in multi-objective scenarios: A comprehensive review
Farooq, B., & Manocha, A. (2024) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2024.101168 · Cited by 29 · see citing works
Remote Sensing Image Classification Using Deep–Shallow Learning
Dou, P., Shen, H., Li, Z., Guan, X., & Huang, W. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3062635 · Cited by 29 · see citing works
Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018
Ou, C., Yang, J., Du, Z., Zhang, T., Niu, B., Feng, Q., Liu, Y., & Zhu, D. (2021) · Remote Sensing · doi:10.3390/rs13234830 · Cited by 24 · see citing works
3DCD: A NEW DATASET FOR 2D AND 3D CHANGE DETECTION USING DEEP LEARNING TECHNIQUES
Coletta, V., Marsocci, V., & Ravanelli, R. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b3-2022-1349-2022 · Cited by 20 · see citing works
Naik, N. N., Chandrasekaran, K., Stateczny, A., Venkatesan, M., & Panneer, P. (2022) · Remote Sensing · doi:10.3390/rs14205232 · Cited by 20 · see citing works
Domain Alignment Dynamic Spectral and Spatial Feature Fusion for Hyperspectral Change Detection
Qin, X., Zhang, Y., & Dong, Y. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3495217 · Cited by 20 · see citing works
Li, X. J., Tian, J., Li, X., Yu, Y., Ou, Y., Zhu, L., Zhu, X., Zhou, B., & Gong, H. (2024) · International Journal of Remote Sensing · doi:10.1080/01431161.2024.2343136 · Cited by 17 · see citing works
Self-citationRFL-CDNet: Towards accurate change detection via richer feature learning
Gan, Y., Xuan, W., Chen, H., Liu, J., & Du, B. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110515 · Cited by 15 · see citing works
Change detection in very high-resolution images based on ensemble CNNs
Zhang, X., Fan, R., Ma, L., Liao, X., & Chen, X. (2020) · International Journal of Remote Sensing · doi:10.1080/01431161.2020.1723818 · Cited by 12 · see citing works
Building Instance Classification using Social Media Images
Hoffmann, E. J., Werner, M., & Zhu, X. X. (2019) · Joint Urban Remote Sensing Event (JURSE) · doi:10.1109/jurse.2019.8809056 · Cited by 10 · see citing works
Self-citationPseudolabel Guided Kernel Learning for Hyperspectral Image Classification
Yang, S., Hou, J., Jia, Y., Mei, S., & Du, Q. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2895070 · Cited by 9 · see citing works
Classification of Compressed Remote Sensing Multispectral Images via Convolutional Neural Networks
Giannopoulos, M., Aidini, A., Pentari, A., Fotiadou, K., & Tsakalides, P. (2020) · Journal of Imaging · doi:10.3390/jimaging6040024 · Cited by 9 · see citing works
Tahermanesh, S., Mokhtarzade, M., & Beirami, B. A. (2025) · Advances in Space Research · doi:10.1016/j.asr.2025.02.046 · Cited by 8 · see citing works
Coca-Castro, A., Zaraza-Aguilera, M. A., Miranda, Y. T. B., Montilla, M., Posada-Fandiño, H. B., Avendaño-Gomez, A. L., Hernández-Hamón, H., Martínez, S. C. G., & Franco, C. (2021) · Revista de teledetección: Revista de la Asociación Española de Teledetección · doi:10.4995/raet.2021.15026 · Cited by 8 · see citing works
Hierarchical CNN Classification of Hyperspectral Images Based on 3-D Attention Soft Augmentation
Miao, X., Zhang, Y., Zhang, J., & Liang, X. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3174301 · Cited by 8 · see citing works
Parcel-Based Sugarcane Mapping Using Smoothed Sentinel-1 Time Series Data
Li, H., Wang, Z., Sun, L., Zhao, L., Zhao, Y., Li, X., Han, Y., Liang, S., & Chen, J. (2024) · Remote Sensing · doi:10.3390/rs16152785 · Cited by 8 · see citing works
Self-citationLu, G.-W., Yao, S., Li, Y., Tang, J., Kan, G., Sun, T., Liu, C., Cheng, D., & Wei, R. (2025) · Remote Sensing · doi:10.3390/rs17101749 · Cited by 7 · see citing works
Wang, Y., Kong, X., Guo, K., Zhao, C., & Zhao, J. (2023) · Agriculture · doi:10.3390/agriculture13071283 · Cited by 7 · see citing works
Cascaded U-Net with Training Wheel Attention Module for Change Detection in Satellite Images
Adil, E., Yang, X., Huang, P., Liu, X., Tan, W., & Yang, J. (2022) · Remote Sensing · doi:10.3390/rs14246361 · Cited by 6 · see citing works
De, J. (2025) · Urban Ecosystems · doi:10.1007/s11252-025-01800-9 · Cited by 5 · see citing works
Change detection with incorporating multi-constraints and loss weights
Zhang, C., & Liu, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108163 · Cited by 5 · see citing works
Bakhti, K., & Larabi, M. E. A. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9553175 · Cited by 5 · see citing works
Adaptive Multiscale Superpixel Embedding Convolutional Neural Network for Land Use Classification
Zhang, H., Altham, C., Trovati, M., Zhang, C., Rolland, I., Lawal, L., Wegbu, D., & Ajienka, N. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3203234 · Cited by 4 · see citing works
Chai, B. (2026) · Habitat International · doi:10.1016/j.habitatint.2025.103702 · Cited by 3 · see citing works
Urban built-up areas extraction by the multiscale stacked denoising autoencoder technique
Mi, X., Cao, W., Yang, J., Li, Z., Zhang, Y., Li, Q., Sun, Z., & Zhan, Y. (2020) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.14.032607 · Cited by 3 · see citing works
Wu, H., Yuan, M., & Zhan, T. (2024) · IET Image Processing · doi:10.1049/ipr2.13037 · Cited by 3 · see citing works
Zamalloa, G. R. P., Tan, Y., & He, L. (2025) · Cities · doi:10.1016/j.cities.2025.106660 · Cited by 2 · see citing works
Deep learning processing of remotely sensed multi-spectral images
Restaino, R., Carbone, A., & Vivone, G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-326484-9.00012-9 · Cited by 1 · see citing works
LSTM AutoEncoders Applied to Semi-Supervised Crop Classification
Teloglu, H. K., & Aptoula, E. (2021) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu53274.2021.9477933 · Cited by 1 · see citing works
Rreddy, E. K. (2022) · Global Journal of Computer Science and Technology · doi:10.34257/gjcstdvol22is2pg53 · Cited by 1 · see citing works
Survey of hyperspectral image change detection method
Ding, C., Chen, J., Zheng, M., Lei Zhang, A., Wei, W., & Zhang, Y. (2024) · Journal of Image and Graphics · doi:10.11834/jig.240031 · Cited by 1 · see citing works
Co-scale cross-attentional transformer for rearrangement target detection
Matsuo, H., Ishikawa, S., & Sugiura, K. (2024) · Advanced Robotics · doi:10.1080/01691864.2024.2381812 · Cited by 1 · see citing works
Feature fusion network based on siamese structure for change detection
Wang, G., Yingying, D., Zhang, T., Lin, J., & Chen, L. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-211432
Liu, X., Shi, H., Zhang, Y., Hou, Y., Niu, L., Zhu, E., Jia, J., Zhao, X., & Wang, L. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-71716-1_18
Change detection and monitoring
Zhang, M., Wang, L., Shi, W., & Zhang, X. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-329152-4.00013-1
- Authors
- Lyu, H., Lu, H., Mou, L., Li, W., Wright, J., Li, X., Li, X., Zhu, X., Wang, J., Yu, L., & Gong, P.
- Affiliations
- Tsinghua University (CN)
- Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
- Iowa State University (US)
- Chinese Academy of Sciences (CN)
- Corresponding author
- Lu, Hui
- ORCID
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2018-03-17
- Volume
- 10
- Issue
- 3
- Pages
- 471
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2789688775
- Cited by
- 70
- References
- 58
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 12 · 2023: 6 · 2022: 10 · 2021: 11 · 2020: 8 · 2019: 10 · 2018: 6
- FWCI
- 6.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- elib (German Aerospace Center) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (HORIZON2020)
- National Natural Science Foundation of China (ERC-2016-StG-714087, 91537210, 2015CB953703, 2017YFA0603703, 91747101)
- Tsinghua National Laboratory for Information Science and Technology
- China Scholarship Council (ERC-2016-StG-714087)
- U.S. Geological Survey
- National Key Research and Development Program of China (2015CB953703, 2017YFA0603703)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- urban change detection, urban cartography, Landsat time series, transfer learning, recurrent neural networks, limited training data, urban land cover, long-term satellite monitoring, Beijing, New York, Melbourne, Munich
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
