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

27 citations as recorded by crossref.

1. Fully convolutional recurrent networks for multidate crop recognition from multitemporal image sequences J. Chamorro Martinez et al. 10.1016/j.isprsjprs.2020.11.007
2. Plant Disease Detection and Classification by Deep Learning—A Review L. Li et al. 10.1109/ACCESS.2021.3069646
3. DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation T. Lin et al. 10.1088/1748-9326/ab66cb
4. ExtremeEarth Meets Satellite Data From Space D. Hagos et al. 10.1109/JSTARS.2021.3107982
5. Developing Long Time Series 1-km Land Cover Maps From 5-km AVHRR Data Using a Super-Resolution Method H. Wang et al. 10.1109/TGRS.2020.3018109
6. Deep Learning for Satellite Image Time-Series Analysis: A review L. Miller et al. 10.1109/MGRS.2024.3393010
7. Understanding deep learning in land use classification based on Sentinel-2 time series M. Campos-Taberner et al. 10.1038/s41598-020-74215-5
8. Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models C. Cao et al. 10.3390/su11195376
9. 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 10.1080/10106049.2022.2057595
10. Deep learning and process understanding for data-driven Earth system science M. Reichstein et al. 10.1038/s41586-019-0912-1
11. Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting S. Mouatadid et al. 10.1016/j.agwat.2019.03.045
12. Using satellite imagery to understand and promote sustainable development M. Burke et al. 10.1126/science.abe8628
13. 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. 10.1007/s10661-024-12598-y
14. 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. 10.1371/journal.pone.0239746
15. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata Y. Zhai et al. 10.1080/13658816.2020.1711915
16. A near real-time deep learning approach for detecting rice phenology based on UAV images Q. Yang et al. 10.1016/j.agrformet.2020.107938
17. Joint Supervised Classification and Reconstruction of Irregularly Sampled Satellite Image Times Series A. Constantin et al. 10.1109/TGRS.2021.3076667
18. Advancing transnational assessments of biodiversity drivers in European agriculture with an updated hierarchical crop and agriculture taxonomy (HCAT) M. Schneider et al. 10.1038/s44264-024-00037-x
19. Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics A. Zhu et al. 10.3390/rs13071360
20. 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 10.1007/s12524-024-01839-9
21. Meta-analysis of deep neural networks in remote sensing: A comparative study of mono-temporal classification to support vector machines S. Heydari & G. Mountrakis 10.1016/j.isprsjprs.2019.04.016
22. Comparisons of Multi Resolution Based AI Training Data and Algorithms Using Remote Sensing Focus on Landcover S. Lee & M. Lee 10.3389/frsen.2022.832753
23. Semantic segmentation of satellite images for crop type identification in smallholder farms P. Buttar & M. Sachan 10.1007/s11227-023-05533-4
24. 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 10.1016/j.isprsjprs.2024.04.027
25. 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. 10.1016/j.compag.2021.106616
26. 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 10.1016/j.isprsjprs.2023.05.005
27. Machine learning in agricultural and applied economics H. Storm et al. 10.1093/erae/jbz033