1. Alexander, C. (2020). Normalized difference spectral indices and urban land cover as indicators of land surface temperature (LST). International Journal of Applied Earth Observation and Geoinformation, 86, 102013. [DOI:10.1016/j.jag.2019.102013]

2. Reis, S. (2008). Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors, 8(10), 6188-6202. [DOI:10.3390/s8106188]

3. Lv, Q., Dou, Y., Niu, X., Xu, J., Xu, J., & Xia, F. (2015). Urban land use and land cover classification using remotely sensed SAR data through deep belief networks. Journal of Sensors, 2015. [DOI:10.1155/2015/538063]

4. Marschner, F. J., & Anderson, J. R. (1967). Major land uses in the United States. US Geological Survey.

5. Jozdani, S. E., Johnson, B. A., & Chen, D. (2019). Comparing deep neural networks, ensemble classifiers, and support vector machine algorithms for object-based urban land use/land cover classification. Remote Sensing, 11(14), 1713. [DOI:10.3390/rs11141713]

6. Zhang, X., Han, L., Han, L., & Zhu, L. (2020). How well do deep learning-based methods for land cover classification and object detection perform on high resolution remote sensing imagery?. Remote Sensing, 12(3), 417. [DOI:10.3390/rs12030417]

7. Lee, S. H., Han, K. J., Lee, K., Lee, K. J., Oh, K. Y., & Lee, M. J. (2020). Classification of landscape affected by deforestation using high-resolution remote sensing data and deep-learning techniques. Remote Sensing, 12(20), 3372. [DOI:10.3390/rs12203372]

8. Li, W., Fu, H., Yu, L., Gong, P., Feng, D., Li, C., & Clinton, N. (2016). Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping. International journal of remote sensing, 37(23), 5632-5646. [DOI:10.1080/01431161.2016.1246775]

9. Bhatti, U. A., Yu, Z., Yuan, L., Zeeshan, Z., Nawaz, S. A., Bhatti, M., ... & Wen, L. (2020). Geometric algebra applications in geospatial artificial intelligence and remote sensing image processing. IEEE Access, 8, 155783-155796. [DOI:10.1109/ACCESS.2020.3018544]

10. Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166-177. [DOI:10.1016/j.isprsjprs.2019.04.015]

11. Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., ... & Zhang, L. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716. [DOI:10.1016/j.rse.2020.111716]

12. Huang, B., Zhao, B., & Song, Y. (2018). Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214, 73-86. [DOI:10.1016/j.rse.2018.04.050]

13. Digra, M., Dhir, R., & Sharma, N. (2022). Land use land cover classification of remote sensing images based on the deep learning approaches: a statistical analysis and review. Arabian Journal of Geosciences, 15(10), 1003. [DOI:10.1007/s12517-022-10246-8]

14. Tong, X. Y., Xia, G. S., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L. (2020). Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237, 111322. [DOI:10.1016/j.rse.2019.111322]

15. Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., & Zhang, S. (2018). Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery. Sensors, 18(11), 3717. [DOI:10.3390/s18113717]

16. Zhao, J., Wang, L., Yang, H., Wu, P., Wang, B., Pan, C., & Wu, Y. (2022). A land cover classification method for high-resolution remote sensing images based on NDVI deep learning fusion network. Remote Sensing, 14(21), 5455. [DOI:10.3390/rs14215455]

17. Zhang, L., Zhang, L., & Du, B. (2016). Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and remote sensing magazine, 4(2), 22-40. [DOI:10.1109/MGRS.2016.2540798]

18. Cheng, X., He, X., Qiao, M., Li, P., Hu, S., Chang, P., & Tian, Z. (2022). Enhanced contextual representation with deep neural networks for land cover classification based on remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 107, 102706. [DOI:10.1016/j.jag.2022.102706]

19. Kavhu, B., Mashimbye, Z. E., & Luvuno, L. (2021). Climate-based regionalization and inclusion of spectral indices for enhancing transboundary land-use/cover classification using deep learning and machine learning. Remote Sensing, 13(24), 5054. [DOI:10.3390/rs13245054]

20. Storie, C. D., & Henry, C. J. (2018, July). Deep learning neural networks for land use land cover mapping. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 3445-3448). IEEE. [DOI:10.1109/IGARSS.2018.8518619]

21. Luus, F. P., Salmon, B. P., Van den Bergh, F., & Maharaj, B. T. J. (2015). Multiview deep learning for land-use classification. IEEE Geoscience and Remote Sensing Letters, 12(12), 2448-2452. [DOI:10.1109/LGRS.2015.2483680]

22. Campos-Taberner, M., García-Haro, F. J., Martínez, B., Izquierdo-Verdiguier, E., Atzberger, C., Camps-Valls, G., & Gilabert, M. A. (2020). Understanding deep learning in land use classification based on Sentinel-2 time series. Scientific reports, 10(1), 17188. [DOI:10.1038/s41598-020-74215-5]

23. Zhu, M., He, Y., & He, Q. (2019). A review of researches on deep learning in remote sensing application. International Journal of Geosciences, 10(1), 1-11. [DOI:10.4236/ijg.2019.101001]

24. Naushad, R., Kaur, T., & Ghaderpour, E. (2021). Deep transfer learning for land use and land cover classification: A comparative study. Sensors, 21(23), 8083. [DOI:10.3390/s21238083]

25. Alem, A., & Kumar, S. (2020, June). Deep learning methods for land cover and land use classification in remote sensing: A review. In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (pp. 903-908). IEEE. [DOI:10.1109/ICRITO48877.2020.9197824]

26. Aspri, M., Tsagkatakis, G., & Tsakalides, P. (2020). Distributed training and inference of deep learning models for multi-modal land cover classification. Remote Sensing, 12(17), 2670. [DOI:10.3390/rs12172670]

27. Jin, B., Ye, P., Zhang, X., Song, W., & Li, S. (2019). Object-oriented method combined with deep convolutional neural networks for land-use-type classification of remote sensing images. Journal of the Indian Society of Remote Sensing, 47, 951-965. [DOI:10.1007/s12524-019-00945-3]

28. Zang, N., Cao, Y., Wang, Y., Huang, B., Zhang, L., & Mathiopoulos, P. T. (2021). Land-use mapping for high-spatial resolution remote sensing image via deep learning: A review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 5372-5391. [DOI:10.1109/JSTARS.2021.3078631]

29. Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., & Zhang, Y. (2018). Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery. Remote Sensing, 10(7), 1119. [DOI:10.3390/rs10071119]

30. Fukushima, K. (1988). Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural networks, 1(2), 119-130. [DOI:10.1016/0893-6080(88)90014-7]

31. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. [DOI:10.1109/5.726791]

32. Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv preprint arXiv:1803.01164.

33. Erhan, D., Courville, A., Bengio, Y., & Vincent, P. (2010, March). Why does unsupervised pre-training help deep learning?. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 201-208). JMLR Workshop and Conference Proceedings.

34. Vasavi, S., Somagani, H. S., & Sai, Y. (2023). Classification of buildings from VHR satellite images using ensemble of U-Net and ResNet. The Egyptian Journal of Remote Sensing and Space Sciences, 26(4), 937-953. [DOI:10.1016/j.ejrs.2023.11.008]

35. Borawar, L., & Kaur, R. (2023, March). ResNet: Solving vanishing gradient in deep networks. In Proceedings of International Conference on Recent Trends in Computing: ICRTC 2022 (pp. 235-247). Singapore: Springer Nature Singapore. [DOI:10.1007/978-981-19-8825-7_21]

36. Harini, M., Selvavarshini, S., Narmatha, P., Anitha, V., Selvi, S. K., & Manimaran, V. (2024, January). Resnet-50 Integrated with Attention Mechanism for Remote Sensing Classification. In International Conference on Advances in Distributed Computing and Machine Learning (pp. 255-265). Singapore: Springer Nature Singapore. [DOI:10.1007/978-981-97-1841-2_19]

37. Rohith, G., & Kumar, L. S. (2020, June). Remote sensing signature classification of agriculture detection using deep convolution network models. In International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (pp. 343-355). Singapore: Springer Singapore. [DOI:10.1007/978-981-15-6315-7_28]

38. Chen, G., Zhang, X., Tan, X., Cheng, Y., Dai, F., Zhu, K., ... & Wang, Q. (2018). Training small networks for scene classification of remote sensing images via knowledge distillation. Remote Sensing, 10(5), 719. [DOI:10.3390/rs10050719]

39. Chen, F., & Tsou, J. Y. (2022). Assessing the effects of convolutional neural network architectural factors on model performance for remote sensing image classification: An in-depth investigation. International Journal of Applied Earth Observation and Geoinformation, 112, 102865. [DOI:10.1016/j.jag.2022.102865]

40. Dimitrovski, I., Kitanovski, I., Kocev, D., & Simidjievski, N. (2023). Current trends in deep learning for Earth Observation: An open-source benchmark arena for image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 197, 18-35. [DOI:10.1016/j.isprsjprs.2023.01.014]

41. Yamashkina, E. O., Yamashkin, S. A., Platonova, O. V., & Kovalenko, S. M. (2022). Development of a neural network model for spatial data analysis. Rossiiskii Tekhnologicheskii Zhurnal [Russian Technological Journal], (10), 28-37. [DOI:10.32362/2500-316X-2022-10-5-28-37]

42. Mahamunkar, G. S., & Netak, L. D. (2021, December). Comparison of various deep CNN models for land use and land cover classification. In International Conference on Intelligent Human Computer Interaction (pp. 499-510). Cham: Springer International Publishing. [DOI:10.1007/978-3-030-98404-5_46]

43. Zhang, Z., Mi, X., Yang, J., Wei, X., Liu, Y., Yan, J., ... & Yu, T. (2023). Remote sensing image scene classification in hybrid classical-quantum transferring CNN with small samples. Sensors, 23(18), 8010. [DOI:10.3390/s23188010]

44. Cheng, G., Xie, X., Han, J., Guo, L., & Xia, G. S. (2020). Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3735-3756. [DOI:10.1109/JSTARS.2020.3005403]

45. Rousset, G., Despinoy, M., Schindler, K., & Mangeas, M. (2021). Assessment of deep learning techniques for land use land cover classification in southern new Caledonia. Remote Sensing, 13(12), 2257. [DOI:10.3390/rs13122257]

46. Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019). Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2217-2226. [DOI:10.1109/JSTARS.2019.2918242]

47. Neumann, M., Pinto, A. S., Zhai, X., & Houlsby, N. (2019). In-domain representation learning for remote sensing. arXiv preprint arXiv:1911.06721

48. Bhosle, K., & Musande, V. (2019). Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images. Journal of the Indian Society of Remote Sensing, 47(11), 1949-1958. [DOI:10.1007/s12524-019-01041-2]

49. Li, W., Liu, H., Wang, Y., Li, Z., Jia, Y., & Gui, G. (2019). Deep learning-based classification methods for remote sensing images in urban built-up areas. Ieee Access, 7, 36274-36284. [DOI:10.1109/ACCESS.2019.2903127]

50. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88. [DOI:10.1016/j.media.2017.07.005]

51. Stoimchev, M., Kocev, D., & Džeroski, S. (2023). Deep Network Architectures as Feature Extractors for Multi-Label Classification of Remote Sensing Images. Remote Sensing, 15(2), 538. [DOI:10.3390/rs15020538]

52. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). [DOI:10.1109/CVPR.2016.90]

53. Fahmi, H. (2022). Patch based Classification using ResNet for Land Cover changes detection of Batu City. MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology), 14(2), 64-69. [DOI:10.18860/mat.v14i2.20946]

54. Wang, L., Wang, J., Liu, Z., Zhu, J., & Qin, F. (2022). Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification. The Crop Journal, 10(5), 1435-1451. [DOI:10.1016/j.cj.2022.01.009]

55. Hosseiny, B., Abdi, A. M., & Jamali, S. (2022). Urban land use and land cover classification with interpretable machine learning-A case study using Sentinel-2 and auxiliary data. Remote Sensing Applications: Society and Environment, 28, 100843. [DOI:10.1016/j.rsase.2022.100843]

56. Shehab, L. H., Fahmy, O. M., Gasser, S. M., & El-Mahallawy, M. S. (2021). An efficient brain tumor image segmentation based on deep residual networks (ResNets). Journal of King Saud University-Engineering Sciences, 33(6), 404-412. [DOI:10.1016/j.jksues.2020.06.001]

57. Dastour, H., & Hassan, Q. K. (2023). A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification. Sustainability, 15(10), 7854. [DOI:10.3390/su15107854]

58. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

59. Macarringue, L. S., Bolfe, É. L., & Pereira, P. R. M. (2022). Developments in land use and land cover classification techniques in remote sensing: A review. Journal of Geographic Information System, 14(1), 1-28. [DOI:10.4236/jgis.2022.141001]

60. Fan, F., Wang, Y., & Wang, Z. (2008). Temporal and spatial change detecting (1998-2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images. Environmental monitoring and assessment, 137, 127-147. [DOI:10.1007/s10661-007-9734-y]

61. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of environment, 37(1), 35-46. [DOI:10.1016/0034-4257(91)90048-B]

62. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote sensing of environment, 80(1), 185-201. [DOI:10.1016/S0034-4257(01)00295-4]

63. Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(04), 611. [DOI:10.4236/ijg.2017.84033]

64. Tilahun, A., & Teferie, B. (2015). Accuracy assessment of land use land cover classification using Google Earth. American Journal of Environmental Protection, 4(4), 193-198. [DOI:10.11648/j.ajep.20150404.14]

65. Liu, Y., Fan, B., Wang, L., Bai, J., Xiang, S., & Pan, C. (2018). Semantic labeling in very high resolution images via a self-cascaded convolutional neural network. ISPRS journal of photogrammetry and remote sensing, 145, 78-95. [DOI:10.1016/j.isprsjprs.2017.12.007]

66. Temenos, A., Temenos, N., Kaselimi, M., Doulamis, A., & Doulamis, N. (2023). Interpretable deep learning framework for land use and land cover classification in remote sensing using SHAP. IEEE Geoscience and Remote Sensing Letters, 20, 1-5. [DOI:10.1109/LGRS.2023.3251652]

67. Huang, X., Yang, D., He, Y., Nelson, P., Low, R., McBride, S., ... & Guarraia, M. (2023). Land cover mapping via crowdsourced multi-directional views: The more directional views, the better. International Journal of Applied Earth Observation and Geoinformation, 122, 103382. [DOI:10.1016/j.jag.2023.103382]

68. Cherif, E., Hell, M., & Brandmeier, M. (2022). DeepForest: Novel deep learning models for land use and land cover classification using multi-temporal and-modal sentinel data of the amazon basin. remote sensing, 14(19), 5000. [DOI:10.3390/rs14195000]

69. Abdu, H. A. (2019). Classification accuracy and trend assessments of land cover-land use changes from principal components of land satellite images. International Journal of Remote Sensing, 40(4), 1275-1300. [DOI:10.1080/01431161.2018.1524587]

70. Hay, A. M. (1988). The derivation of global estimates from a confusion matrix. International Journal of Remote Sensing, 9(8), 1395-1398. [DOI:10.1080/01431168808954945]

71. Abdelkareem, O. E. A., Elamin, H. M. A., Eltahir, M. E. S., Adam, H. E., Elhaja, M. E., Rahamtalla, A. M., ... & Elmar, C. (2018). Accuracy assessment of land use land cover in Umabdalla natural reserved forest, South Kordofan, Sudan. International journal of agricultural and environmental sciences, 3(1), 5-9.

72. Narkhede, M. V., Bartakke, P. P., & Sutaone, M. S. (2022). A review on weight initialization strategies for neural networks. Artificial intelligence review, 55(1), 291-322. [DOI:10.1007/s10462-021-10033-z]

73. Desai, C. (2024). Impact of Weight Initialization Techniques on Neural Network Efficiency and Performance: A Case Study with MNIST Dataset. International Journal Of Engineering And Computer Science, 13(04).

74. Li, H., Krček, M., & Perin, G. (2020). A comparison of weight initializers in deep learning-based side-channel analysis. In Applied Cryptography and Network Security Workshops: ACNS 2020 Satellite Workshops, AIBlock, AIHWS, AIoTS, Cloud S&P, SCI, SecMT, and SiMLA, Rome, Italy, October 19-22, 2020, Proceedings 18 (pp. 126-143). Springer International Publishing. [DOI:10.1007/978-3-030-61638-0_8]