## 4 General Considerations, Open Issues, and Future Perspectives  
The machine learning techniques applied for detecting aerosols and clouds from lidar observations are still in their early stages but hold great potential for future advancement. In Table 3, all the different machine learning algorithms implemented to detect, classify, and study atmospheric aerosol layers, clouds, and boundary layers are summarized, together with the observation platform and lidar type. This table highlights that the detection and classification of aerosols has received much more attention with respect to clouds. ML algorithms were developed mostly for satellites and aircraft observations. Algorithm applications to networks of lidars have mostly been dedicated to mixing and planetary boundary layer detection.  
As a general remark for future algorithm implementation, it is important to exercise caution when applying existing machine learning algorithms without proper consideration. It is crucial to always consider the underlying physics processes that govern the interaction of light with atmospheric constituents. Machine learning models must always be designed and trained not as a black box, but based on correct reasoning, taking into account real-world relationships, to ensure accurate and meaningful results.. This ensures that trained models are capable of distinguishing between various aerosol species, such as dust, smoke, and pollution, as well as different types of clouds, including liquid and ice clouds. Additionally, understanding the physics behind lidar measurements enables the development of machine learning algorithms that can exploit different lidar techniques, which will extract relevant information from lidar profiles, such as backscatter, extinction, depolarization ratio, and spectral characteristics, to enhance the accuracy of classification. Although machine learning techniques offer the potential to automate aerosol and cloud detection, it is important to recognize the limitations and uncertainties associated with these approaches. Indeed, machine learning algorithms should be trained on diverse and representative datasets that cover a wide range of atmospheric conditions and aerosol/cloud types, to enhance their robustness and applicability. Furthermore, regular validation and evaluation against independent measurements and ground-based observations are necessary, to assess the accuracy and reliability of the classification results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Type and Refs** & **Platform** & **Lidar** & **Network** \\ \hline \multirow{6}{*}{Aerosols} & D, C - CNN; McGill [55] & Aircraft & HSRL & No \\  & D - CNN; [PERSON] et al. [58] & Satellite & EL & No \\  & C - CNN; [PERSON] et al. [59] & Satellite & EL & No \\  & D - RF, GBT; [PERSON] et al. [63] & Ground & DWL & No \\  & C - ANN; [PERSON] et al. [62] & Ground & ML & Yes \\ \hline \multirow{2}{*}{Clouds, Rain} & C - RF, GBT [PERSON] et al. [63] & Ground & DWL & No \\  & D - CNN; [PERSON] et al. [58] & Satellite & HSRL & No \\ \hline \multirow{3}{*}{PBLH, MLH, AOD} & AOD - ANN [PERSON], [PERSON] [68] & Satellite & EL & No \\  & MLH - KM, GBT [PERSON] et al. [64] & Ground & EL & Yes \\ \cline{1-1}  & PBLH - CNN, [PERSON] et al. [66] & Ground & EL & Yes \\ \cline{1-1}  & PBLH - CNN, [PERSON] et al. [67] & Satellite & EL & No \\ \hline \hline \end{tabular}
\end{table}
Table 3: D = Detection; C = Classification; CNN = Convolution Neural Network; RF = Random Forest; GBT = Gradient Booster Trees; ANN = Artificial Neural Network; KM = K-Means; HSRL = High Spectral Resolution Lidar [20]; DWL = Doppler Wind Lidar [21]; EL = Elastic Lidar [5]; RL = Raman Lidar [12]; MLH = Mixing Layer Height; PBLH = Planetary Boundary Layer Height; AOD = Aerosol Optical Depth.