## 3 Theory and Methods
### Convolutional LSTM Networks
The categorical variable \"State\" describes the general condition of the road surface, with values ranging from 1 to 6 for Dry, Moist, Wet, Icy, Snowy, and Slushy. We divide the dataset into training and test sets and use deep learning classification techniques to create RMSDC. The ConvLSTM architecture is used to capture temporal dependencies and interactions in data--notably, those that exist between input features  
Figure 2: **(a)** Detailed ConvLSTM block. (**b**) Each block’s variable adheres to the convention of listing the layer’s name, its activation, and an integer to indicate how many kernels were utilized in that block. across time. The RMSDC model mixes road weather and sensor data, effectively adapting to climate change projections and calculating the root-mean deviation (RMSD). The goal of the RMSDC (Road Maintenance and Smart Data Collection) is to create a dynamic framework that enables the development of rules for opening and closing roads based on changing weather and traffic data. These policies should ensure smooth traffic flow while keeping road maintenance costs within a predetermined budget. This section provides a detailed explanation of our proposed RMSDC technique and the suggested deep architecture. This paper addresses the significant challenge statistical downscaling approaches face in effectively capturing spatiotemporal dependencies. We present a method that leverages recurrent convolutional LSTM to downscale ensembles of Smart Roads outputs, considering multiple starting conditions. Additionally, we propose a study approach to augment various state variables. To achieve this, we introduce additional variables related to weather conditions, such as surface temperature, wind speed, and air temperature. These weather variables play a crucial role, as heavy rain can lead to floods and landslides, which can impact the condition of the road. The road is characterized by parameters such as estimated traffic volume, maintenance budget, and whether it is paved or unpaved. The present weather and traffic conditions influence the road's state. If the model keeps the road open, traffic can utilize it, whereas if it is closed, maintenance activities can be carried out to restore the road to its original condition. Spatial Parameters--Latitude and Longitude: These geographic coordinates describe the measurement site's location. Examine how road surface conditions differ according to latitude and longitude, discovering places with distinct State categories. Height: The elevation above sea level indicates the altitude of the place. Investigate how road surface conditions change with elevation, particularly for State categories such as Icy or Snowy, which higher elevations may alter. Precision: The GPS measurement's precision reflects the location data's dependability. Consider whether the accuracy influences the classification of road surface conditions and the spatial distribution of State categories. Temporal parameters--Date and Time: These parameters indicate the dataset's temporal aspect. Examine how road surface conditions change over time and on different days. Recognize patterns associated with seasons, weekdays, or specific times of day. Seasonal Variations: Examine how the State categories fluctuate throughout the year. Examine whether certain road surface conditions, such as icy or snowy, are more common throughout specific months. Examine how the road surface conditions change throughout the day. State types are more prevalent in the morning or at night. Distance: The distance traveled by the vehicle, since the previous measurement indicates the sample frequency. Examine how the distance between data affects temporal resolution and road surface condition assessments. We propose a Super Resolution approach based on Convolutional LSTM to statistically downscale climatic data from coarse-resolution to fine-resolution observation data. This approach considers the spatial and temporal dependencies between the target and auxiliary variables.