## 8 Discussion and Conclusions  
This research paper proposes a highly effective geographic object-based scene classification system for image _Remote Sensing_ which employs a novel ZsL architecture. It introduces serious prerequisites for even more sophisticated pattern recognition systems without prior training. To the best of our knowledge, it is the first time that such an algorithm has been presented in the literature. It facilitates learning of specialized intermediate representation extraction functions, under complex Deep Learning architectures. Additionally, it utilizes first and second order derivatives as a pre-training method for learning parameters which do not cause exploding or diminishing gradients. Finally, it avoids potential overfitting, while it significantly reduces computational costs and training time. It produces improved training stability, high overall performance and remarkable classification accuracy. Likewise, the ensemble method used leads to much better prediction results, while providing generalization which is one of the key requirements in the field of machine learning [38]. At the same time, it reduces bias and variance and it eliminates overfitting, by implementing a robust model capable of responding to high complexity problems. The proposed MAME-ZsL algorithm follows a heuristic hierarchical hyperparameter search methodology, using intermediate representations extracted from the employed neural network and avoiding other irrelevant ones. Using these elements, it discovers appropriate representations which can correctly classify unknown image samples. What should also be emphasized is the use of bootstrap sampling, which accurately addresses noisy scattered misclassification points which other spectral classification methods cannot handle. The implementation of MAME-ZsL, utilizing the MAML++ algorithm, is based on the optimal use and combination of two highly efficient and fast learning processes (_Softmax_ activation function and _AdaBound_ algorithm) which create an integrated intelligent system. It is the first time which this hybrid approach has been introduced in the literature. The successful choice of the _Softmax_ (SFM) function instead of the _Sigmoid_ (SIG) was based on the fact that it performs better on multi-classification problems, such as the one under consideration, and the sum of its probabilities equals to 1. On the other hand, the _Sigmoid_ is used for binary classification tasks. Finally, in the case of SFM, high values have the highest probabilities, whereas in SIG this is not the case. The use of the AdaBound algorithm offers high convergence speed compared to stochastic gradient descent models. Moreover, it exceedes the poor generalization ability of the adaptive approaches, as it has dynamic limits on the Learning Rate in order to obtain the highest accuracy for the dataset under consideration. Still, this network remarkably implements a GeoAI approach for large-scale geospatial data analysis which attempts to balance latency, throughput and fault-tolerance using ZsL. At the same time, it makes effective use of the intermediate representations of Deep Learning. The proposed model avoids overfitting, decreases variance or bias, and it can fit unseen patterns, without reducing its performance. The reliability of the proposed network has been proven in identifying scenes from Remote Sensing photographs. This suggests that it can be used in higher level geospatial data analysis processes, such as multi-sector classification, recognition and monitoring of specific patterns and sensors' data fusion. In addition, the performance of the proposed model was evaluated against state-of-the-art supervised Deep Learning models. It is worth mentioning that the proposed model was trained with instances only from seven classes, while the other models were trained with instances from all eleven classes.