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.
2. Weakly-supervised domain adaptation for built-up region segmentation in aerial and satellite imagery J. Iqbal & M. Ali
3. Evaluation of CNN model by comparing with convolutional autoencoder and deep neural network for crop classification on hyperspectral imagery K. Bhosle & V. Musande
4. Possibilities and limitations of convolutional neural network machine learning architectures in the characterisation of achiral orthogonal smectic liquid crystals R. Betts & I. Dierking
5. Weighted split-flow network auxiliary with hierarchical multitasking for urban land use classification of high-resolution remote sensing images G. He et al.
6. Sustainability Analysis of Soil Erosion Control in Rwanda: Case Study of the Sebeya Watershed F. Majoro et al.
7. ASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land cover classification P. Zhang et al.
8. A novel approach to use semantic segmentation based deep learning networks to classify multi-temporal SAR data A. Mehra et al.
9. Comparative Validation and Misclassification Diagnosis of 30-Meter Land Cover Datasets in China X. Xu et al.
10. Object based convolutional neural network for cloud classification in very high-resolution hyperspectral imagery R. Rizkiyanto et al.
11. Deep built-structure counting in satellite imagery using attention based re-weighting A. Shakeel et al.
12. Evaluating Resilience-Centered Development Interventions with Remote Sensing N. Kerle et al.
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.
14. An improved categorical cross entropy for remote sensing image classification based on noisy labels P. Li et al.
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
16. Study on the Extraction of Land Cover Information From Multisource Remote Sensing Data for Refined Management of National Parks B. Zhou et al.
17. A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery V. Alhassan et al.
18. How computer vision can facilitate flood management: A systematic review U. Iqbal et al.
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.
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
21. Assessing the Extent of Historical, Current, and Future Land Use Systems in Uganda M. Mwanjalolo et al.
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
23. Deep learning for geometric and semantic tasks in photogrammetry and remote sensing C. Heipke & F. Rottensteiner
24. Development of a deep convolutional neural network model for detection and delineation of coal mining regions A. Kumar & A. Gorai
25. Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape M. Aghababaei et al.