###### Abstract  
Unmanned aerial vehicles (UAVs) have significant abilities for automatic detection and mapping of urban surface materials due to their high resolution. It requires a massive quantity of data to understand the ground material properties. In recent days, computer vision based approaches for intelligent transportation systems (ITS) have gained considerable interest among research communities and business people. Road classification using remote sensing images plays a vital role in urban planning. It remains challenging because of scene complexity, fluctuating road structures, and inappropriate illumination circumstances. The design of intelligent models and other machine learning (ML) approaches for road classification has yet to be further explored. In this aspect, this paper presents a metaheuristics optimization with deep autoencoder enabled road classification model (MODAE-RCM). The presented MODAE-RCM technique mainly focuses on the classification of roads into five types, namely wet, ice, rough, dry, and curvy roads. In order to accomplish this, the presented MODAE-RCM technique exploits modified fruit fly optimization (MFFO) with neural architectural search network (NASNet) for feature extraction. In order to classify roads, an interactive search algorithm (ISA) with a DAE model is used. The exploitation of metaheuristic hyperparameter optimizers helps to improve the classification results. The experimental validation of the MODAE-RCM technique was tested by employing a dataset comprising five road types. The simulation analysis highlighted the superior outcomes of the MODAE-RCM approach to other existing techniques. unmanned aerial vehicles; ITS; smart cities; road classification; computer vision +
Footnote †: journal: Applied Sciences  
and consumes more time. Hyperspectral image, otherwise called imaging spectrometry, can be defined as acquiring data in several narrow, contiguous spectral bands. It offers detailed data compared to other RS methods [3]. Various chemical materials, such as gravel and asphalt, are extracted on a detailed level through hyperspectral images by their respective physical properties (reflectivity, absorption, albedo, and many more). This feature will be useful to discriminate and extract objects of cities, particularly those having the same spectral property [4]. Road surface materials are detected through hyperspectral images, incurring less cost than field surveying. Many existing approaches to map roads can be either semi-automatic or manual [5]. However, such techniques will be expensive and consume more time. Particularly, they might include more field work and clarification of aerial images, through which limited data is obtained. Hyperspectral data contains substantial potentiality relating to automatic detection of road surface materials [6]. However, no standard technique to map road surfaces or find the state of road surface resources has existed until now. Many existing techniques were first formulated for mineral detection. Therefore, it will be difficult to leverage such techniques in detecting road surface materials, because of the difference in such resources in the case of roads in smaller regions [7]. General road categories include ice, dry, rough, and curvy road surfaces. Classification of these road forms will be a fascinating issue. In this context, several image processing methods are used for assisting smart vehicles and for classifying various road categories [8]. However, the road image quality is affected through dynamic weather, illumination variation, and blurring. Progressive approaches, such as deep learning (DL), are helpful for observing the challenging atmosphere by a computer vision (CV) technique, and might help smart vehicles to modify their driving performance depending on the road type [9]. Driving performance mainly disturbs people near road regions, most often if a road has a medium amount of traffic. It is unrealistic to assess the condition of each road which a smart vehicle could approach [10].