MULTI-TEMPORAL LAND COVER CLASSIFICATION WITH LONG SHORT-TERM MEMORY NEURAL NETWORKS

35 citations as recorded by crossref.

1. Fully convolutional recurrent networks for multidate crop recognition from multitemporal image sequences J. Chamorro Martinez et al. https://doi.org/10.1016/j.isprsjprs.2020.11.007
2. Plant Disease Detection and Classification by Deep Learning—A Review L. Li et al. https://doi.org/10.1109/ACCESS.2021.3069646
3. ExtremeEarth Meets Satellite Data From Space D. Hagos et al. https://doi.org/10.1109/JSTARS.2021.3107982
4. Developing Long Time Series 1-km Land Cover Maps From 5-km AVHRR Data Using a Super-Resolution Method H. Wang et al. https://doi.org/10.1109/TGRS.2020.3018109
5. Deep Learning for Satellite Image Time-Series Analysis: A review L. Miller et al. https://doi.org/10.1109/MGRS.2024.3393010
6. Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models C. Cao et al. https://doi.org/10.3390/su11195376
7. Land use and land cover dynamics study in Bahia’s Western Region, Brazil: a remote sensing and deep learning approach H. Sahu & P. Pujari https://doi.org/10.1007/s41870-025-03023-9
8. Deep learning and IoT-based framework for sesame plant identification and weed detection P. Nagaraj et al. https://doi.org/10.1038/s41598-026-47961-1
9. Deep learning and process understanding for data-driven Earth system science M. Reichstein et al. https://doi.org/10.1038/s41586-019-0912-1
10. Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India N. Naik et al. https://doi.org/10.1007/s10661-024-12598-y
11. On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network N. Minallah et al. https://doi.org/10.1371/journal.pone.0239746
12. A near real-time deep learning approach for detecting rice phenology based on UAV images Q. Yang et al. https://doi.org/10.1016/j.agrformet.2020.107938
13. A Multitask Framework With Cross-Task Selective Feature Sharing for Remote Sensing Image Time-Series Analysis M. Atanasova et al. https://doi.org/10.1109/JSTARS.2026.3673598
14. Joint Supervised Classification and Reconstruction of Irregularly Sampled Satellite Image Times Series A. Constantin et al. https://doi.org/10.1109/TGRS.2021.3076667
15. Advancing transnational assessments of biodiversity drivers in European agriculture with an updated hierarchical crop and agriculture taxonomy (HCAT) M. Schneider et al. https://doi.org/10.1038/s44264-024-00037-x
16. Mapping small landscape features in agricultural lands at the national level using CNN-based semantic segmentation M. Fauzan et al. https://doi.org/10.1080/10106049.2026.2691353
17. A seasonal-series LSTM network for irregular urban function zone recognition using Sentinel-2 images T. Hu et al. https://doi.org/10.1016/j.asoc.2025.112876
18. Effect of intra-year Landsat scene availability in land cover land use classification in the conterminous United States using deep neural networks G. Mountrakis & S. Heydari https://doi.org/10.1016/j.isprsjprs.2024.04.027
19. Harvesting the Landsat archive for land cover land use classification using deep neural networks: Comparison with traditional classifiers and multi-sensor benefits G. Mountrakis & S. Heydari https://doi.org/10.1016/j.isprsjprs.2023.05.005
20. Early Detection of Forest Disturbances in Homogeneous Stands – Deep Learning-Based Monitoring of Picea Abies M. Kirsch et al. https://doi.org/10.1109/JSTARS.2025.3603094
21. Wetland Mapping Using Machine Learning and Deep Learning Algorithms: Assessing Spatial Transferability of Recent Approaches S. Maleki & V. Rahdari https://doi.org/10.3390/rs18081234
22. DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation T. Lin et al. https://doi.org/10.1088/1748-9326/ab66cb
23. Understanding deep learning in land use classification based on Sentinel-2 time series M. Campos-Taberner et al. https://doi.org/10.1038/s41598-020-74215-5
24. Application of transfer learning of deep CNN model for classification of time-series satellite images to assess the long-term impacts of coal mining activities on land-use patterns A. Kumar & A. Gorai https://doi.org/10.1080/10106049.2022.2057595
25. STU-Mamba: A U-Mamba With Cross-Scale Alignment and Temporal Adaptive Context Gating for Satellite Image Time Series Segmentation J. Li et al. https://doi.org/10.1109/TGRS.2026.3708102
26. Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting S. Mouatadid et al. https://doi.org/10.1016/j.agwat.2019.03.045
27. Using satellite imagery to understand and promote sustainable development M. Burke et al. https://doi.org/10.1126/science.abe8628
28. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata Y. Zhai et al. https://doi.org/10.1080/13658816.2020.1711915
29. Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics A. Zhu et al. https://doi.org/10.3390/rs13071360
30. Knowledge-Guided Multi-Source Time-Series Approach for Spatially Robust Crop Type Classification N. Xu et al. https://doi.org/10.3390/app16094194
31. Generating Land Cover Maps in Semi-arid Regions Based on a 3D Semantic Segmentation Architecture Using Multi-temporal Sentinel-2 Satellite Images: A Case Study of Ludhiana District in Punjab, India P. Buttar & M. Sachan https://doi.org/10.1007/s12524-024-01839-9
32. Comparisons of Multi Resolution Based AI Training Data and Algorithms Using Remote Sensing Focus on Landcover S. Lee & M. Lee https://doi.org/10.3389/frsen.2022.832753
33. Semantic segmentation of satellite images for crop type identification in smallholder farms P. Buttar & M. Sachan https://doi.org/10.1007/s11227-023-05533-4
34. Improving wheat yield estimates using data augmentation models and remotely sensed biophysical indices within deep neural networks in the Guanzhong Plain, PR China J. Zhang et al. https://doi.org/10.1016/j.compag.2021.106616
35. Machine learning in agricultural and applied economics H. Storm et al. https://doi.org/10.1093/erae/jbz033