Convolutional Autoencoder-Based Deep Learning Approach for Aerosol Emission Detection Using LiDAR Dataset

Table 1

Comparative analysis of previous studies conducted on LiDAR.

Ref	Technique	Source of data	Outcome	Accuracy
Xiu et al. [1]	Semantic segmentation	LiDAR dataset	The entire waveform of LiDAR	89%
Zhang et al. [2]	Computer vision techniques	Land SAT8 data	Retrieval of Forest aboveground biomass	90.3%
Melotti and Premebida [5]	Multimodal deep learning	Self-created dataset	Object recognition combining camera	93%
Zhang et al. [7]	Multisource data fusion	LiDAR dataset	Data fusion	88%
Wahid et al. [6]	Object exploration vision techniques	Self-created dataset	Distributed soft actor critics	86.45%