# Emerging Technologies for Automation in Environmental Sensing: Review
## 3 Emerging Technologies in Environmental Sensing  
### Leveraging Transfer Learning for Enhanced Environmental Sensing  
In today's world, people can barely afford to indulge in investing resources in data assemblage due to its inaccessibility, which is usually expensive and challenging to gather. Consequently, scientists and researchers uncovered more suitable norms of data collection, that is, the transfer of learning or knowledge between the tasks [38]. So, this perspective has encouraged transfer learning (TL) to enhance data collection in machine learning (ML) using the data gathered before they were introduced. Straightforwardly, TL is the refinement or advancement of comprehending a new task through knowledge transfer from a corresponding task that has already been learned, which will lower reliance on many target domain data for making target learners [39; 40]. In TL, the negative transfer of learning usually occurs if the approach adopted reduces the overall performance. Thus, producing the positive transfer by avoiding the negative transfer between corresponding tasks is very essential. Figure 2 depicts the transfer learning (TL) workflow, which involves pre-training a model on a large dataset for a related task, extracting relevant features, and adapting it to a new task by fine-tuning its parameters or using its features as input to a new model. This process accelerates learning and improves performance in the new task, like how existing skills aid in learning new tasks. For instance, just as knowing how to play the violin can help in learning the piano faster, transfer learning allows transferring knowledge from one task to facilitate learning in another, making adaptation quicker and more effective. The classification of TL includes two categories, homogeneous and heterogeneous transfer learning [40]. The strategies related to homogeneous transfer learning are developed and offered for handling situations with identical attributes, otherwise, it is called heterogeneous transfer learning. Also, heterogeneous transfer learning is more complex than homogeneous transfer learning due to its requirement of feature space adaptation [41]. Transfer learning in computer vision makes use of pre-trained models and datasets to effectively address new tasks. By utilizing a pre-trained model as a foundational framework, known as a backbone model, practitioners minimize the necessity for extensive new data and annotations, thereby enhancing performance on the target task. This strategy streamlines the learning process, mitigating challenges such as data scarcity and annotation expenses. Drawing parallels with human learning, where skills transfer across contexts, pre-trained neural networks such as VGG, ResNet, Inception, and MobileNet are widely employed as starting points. These models, accessible via platforms like TensorFlow Model Garden and PyTorch Hub, facilitate efficient model adaptation [42; 43; 44; 45]. Both local and cloud-based training strategies offer benefits, with local training granting control over configurations and cloud-based training providing scalability and potentially expedited processing times. Table 1 summarizes the key features of various neural network architecture. Transfer learning relies on three aspects: what, how, and when the data should be transferred. Due to the variability of the settings in the source dataset and target data in the transfer learning, the transfer learning subsets can be classified as inductive, transductive, and unsupervised learning [47]. When the tasks are dissimilar, any resemblance between the source and target domains is called inductive learning. Again, when the tasks are identical, but the feature spaces are different, it is called transductive learning, and when no labeled data can be used for training, it is called unsupervised learning. The various transfer learning methods include fine-tuning, domain adaptation, multi-task learning, feature extraction, training the model, the direct use of a pre-trained model, etc.