94 citations as recorded by crossref.

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44. Visual Evaluation of Urban Streetscape Design Supported by Multisource Data and Deep Learning G. Feng et al. https://doi.org/10.1155/2022/3287117
45. Multi-task convolutional neural networks outperformed random forest for mapping soil particle size fractions in central Iran R. Taghizadeh-Mehrjardi et al. https://doi.org/10.1016/j.geoderma.2020.114552
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48. Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al. https://doi.org/10.1111/ejss.13463
49. A Study on Digital Soil Mapping Based on Multi-Attention Convolutional Neural Networks: A Case Study in Heilongjiang Province Y. Liu et al. https://doi.org/10.3390/agriculture15212273
50. Evaluation of projected soil organic carbon stocks under future climate and land cover changes in South Africa using a deep learning approach O. Odebiri et al. https://doi.org/10.1016/j.jenvman.2022.117127
51. A novel framework for improving soil organic matter prediction accuracy in cropland by integrating soil, vegetation and human activity information J. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.166112
52. Proximal and remote sensor data fusion for 3D imaging of infertile and acidic soil J. Wang et al. https://doi.org/10.1016/j.geoderma.2022.115972
53. Integrating additional spectroscopically inferred soil data improves the accuracy of digital soil mapping S. Chen et al. https://doi.org/10.1016/j.geoderma.2023.116467
54. Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks O. Odebiri et al. https://doi.org/10.1016/j.scitotenv.2022.161150
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56. A generative deep neural network as an alternative to co-kriging H. Rakotonirina et al. https://doi.org/10.1016/j.acags.2024.100198
57. A Novel Self-Supervised Learning Method for Accurate Multigranularity Prediction of Soil Organic Carbon Z. Yu et al. https://doi.org/10.1109/TGRS.2024.3511118
58. A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics X. Zhang et al. https://doi.org/10.1016/j.ecoinf.2024.102923
59. Multi-Modal Vision Transformer for high-resolution soil texture prediction of German agricultural soils using remote sensing imagery L. Wittstruck et al. https://doi.org/10.1016/j.rse.2025.114985
60. Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. https://doi.org/10.1016/j.catena.2021.105299
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64. Soil parent material prediction through satellite multispectral analysis on a regional scale at the Western Paulista Plateau, Brazil F. Mello et al. https://doi.org/10.1016/j.geodrs.2021.e00412
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70. Comparison of Machine Learning-Based Prediction of Qualitative and Quantitative Digital Soil-Mapping Approaches for Eastern Districts of Tamil Nadu, India R. Kumaraperumal et al. https://doi.org/10.3390/land11122279
71. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review S. Chen et al. https://doi.org/10.1016/j.geoderma.2021.115567
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