# An Introduction to Machine and Deep Learning Methods for Cloud Masking Applications
## 6 Challenges and Future Prospects in Cloud Masking Applications  
Cloud detection and masking play pivotal roles in the process of cloud and atmospheric correction for remote sensing data. Employing techniques based on spectral, spatial, temporal, or contextual criteria (or their combinations) is commonplace for identifying and eliminating pixels affected by clouds, cloud shadows, or haze. However, this intricate process is susceptible to errors and uncertainties, presenting challenges such as cloud misjudgement with other elements. These challenges can significantly impact subsequent analyses and applications. Despite these difficulties, there exists a promising avenue for enhancing cloud masking techniques. Future prospects involve the development of advanced and robust cloud detection and masking methods capable of handling complex cloud scenarios by leveraging multiple sensors and platforms.  
The accuracy of cloud segmentation has been improved with the introduction of Machine Learning methods, but in particular with the exploitation of the advantages offered by the inherent properties of Deep Learning. The dependence of these methods on the availability of large reference datasets for training and testing curbs the potential of the approach to date. Furthermore, the development of cloud segmentation methods for multi-sensor images is currently slowed down by the close relationship between the images and the characteristics of each sensor. It requires extensive reference data to train the models. Improvements in transfer learning techniques and data adaptability can benefit to address the problem. An important step forward in this direction is represented by the work in [119], where the sensor independence of a model is highlighted to make better use of existing data, increase the efficiency and applicability of models, and ultimately contribute to more effective and versatile remote sensing analysis.  
Finally, the implementation of rigorous and reliable quality assessment and validation techniques emerges as a crucial aspect to measure the quality of corrected data and support informed decision-making processes.  
To conclude, some considerations that recent studies have highlighted in assessing environmental and economic costs. The energy consumption of Deep Learning methods, which are crucial for processing large datasets, is often assumed to increase exponentially with the growth in the number of parameters [132]. However, this perspective shifts when focusing on inference costs rather than training costs. Inference, due to its multiplicative factors, accounts for a significant portion of computing effort in AI applications, including cloud masking. Contrary to expectations, for a sustained increase in AI performance, particularly in areas such as computer vision and natural language processing relevant to remote sensing, there is a much softer growth in energy consumption [133]. This can be attributed to both algorithmic innovations and advancements in hardware, which not only offer higher FLOPS but also bring significant energy efficiency optimizations. These developments suggest that while the initial environmental impact and costs of advanced techniques might be high, their long-term efficiency and cost-effectiveness are enhanced by these technological improvements. Despite this, the increasing pervasiveness of AI in various applications, including remote sensing, could potentially multiply the overall energy consumption, a factor that must be carefully considered in the broader environmental impact assessment [133]. Future work in cloud masking should increase performance evaluations from this point of view as well, and not only from the point of view of quantitative accuracy estimation, which is the most popular way so far.