Table 6 Comparative analysis of the proposed MobileNetV3 with MapReduce framework against existing models.
References | Year | Remote sensing | MapReduce | Algorithm | Accuracy |
|---|---|---|---|---|---|
2017 | Yes | No | CNN | 94.60% | |
2017 | No | No | Modified Inception-ResNet CNN | 91% | |
2019 | No | No | LSTM and Conv1D | 85% | |
2019 | No | No | VGG16 and Xception | 86.2% | |
2020 | No | No | ResNet101V2 | 86.7% | |
2020 | No | No | CNN | 91% | |
2020 | Yes | No | CNN-RF | 94.27% | |
2020 | No | No | CNN | 90% | |
2020 | No | No | CNN | 92% | |
2021 | No | No | CNN | 94% | |
2021 | Yes | No | ResNet-18 DCNN classifier | 94% | |
2021 | No | No | CNN | 90% | |
2021 | No | No | ResNet, MobileNet, DenseNet | 93.45% | |
Proposed Model | 2025 | Yes | Yes | MobileNetV3 | 96.8% |
