CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS

25 citations as recorded by crossref.

1. Integrated model for land-use transformation analysis based on multi-layer perception neural network and agent-based model Z. Hashemi Aslani et al. 10.1007/s11356-022-19392-8
2. Weakly-supervised domain adaptation for built-up region segmentation in aerial and satellite imagery J. Iqbal & M. Ali 10.1016/j.isprsjprs.2020.07.001
3. Evaluation of CNN model by comparing with convolutional autoencoder and deep neural network for crop classification on hyperspectral imagery K. Bhosle & V. Musande 10.1080/10106049.2020.1740950
4. Possibilities and limitations of convolutional neural network machine learning architectures in the characterisation of achiral orthogonal smectic liquid crystals R. Betts & I. Dierking 10.1039/D4SM00295D
5. Weighted split-flow network auxiliary with hierarchical multitasking for urban land use classification of high-resolution remote sensing images G. He et al. 10.1080/01431161.2022.2143734
6. Sustainability Analysis of Soil Erosion Control in Rwanda: Case Study of the Sebeya Watershed F. Majoro et al. 10.3390/su15031969
7. ASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land cover classification P. Zhang et al. 10.1016/j.isprsjprs.2024.09.025
8. A novel approach to use semantic segmentation based deep learning networks to classify multi-temporal SAR data A. Mehra et al. 10.1080/10106049.2019.1704072
9. Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China X. Xu et al. 10.3390/rs16224330
10. Object based convolutional neural network for cloud classification in very high-resolution hyperspectral imagery R. Rizkiyanto et al. 10.1088/1755-1315/500/1/012059
11. Deep built-structure counting in satellite imagery using attention based re-weighting A. Shakeel et al. 10.1016/j.isprsjprs.2019.03.014
12. Evaluating Resilience-Centered Development Interventions with Remote Sensing N. Kerle et al. 10.3390/rs11212511
13. Impact of Thailand’s Eastern Economic Corridor (EEC) Policy on Land Use: Prediction Up to 2040 by Combining CNN Land Cover and Machine Learning Methods on Socio-Economic Data D. Gomez et al. 10.1007/s12061-025-09672-4
14. An improved categorical cross entropy for remote sensing image classification based on noisy labels P. Li et al. 10.1016/j.eswa.2022.117296
15. Novel CNN-Based Approach for Reading Urban Form Data in 2D Images: An Application for Predicting Restaurant Location in Seoul, Korea J. Yang & Y. Kwon 10.3390/ijgi12090373
16. Study on the Extraction of Land Cover Information From Multisource Remote Sensing Data for Refined Management of National Parks B. Zhou et al. 10.1109/JSTARS.2025.3601006
17. A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery V. Alhassan et al. 10.1007/s00521-019-04349-9
18. How computer vision can facilitate flood management: A systematic review U. Iqbal et al. 10.1016/j.ijdrr.2020.102030
19. Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning D. Arrechea-Castillo et al. 10.3390/rs15102521
20. A comparative evaluation of deep convolutional neural network and deep neural network-based land use/land cover classifications of mining regions using fused multi-sensor satellite data A. Kumar & A. Kumar Gorai 10.1016/j.asr.2023.08.057
21. Assessing the Extent of Historical, Current, and Future Land Use Systems in Uganda M. Mwanjalolo et al. 10.3390/land7040132
22. Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images K. Bhosle & V. Musande 10.1007/s12524-019-01041-2
23. Deep learning for geometric and semantic tasks in photogrammetry and remote sensing C. Heipke & F. Rottensteiner 10.1080/10095020.2020.1718003
24. Development of a deep convolutional neural network model for detection and delineation of coal mining regions A. Kumar & A. Gorai 10.1007/s12145-023-00955-3
25. Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape M. Aghababaei et al. 10.3390/rs14184452