## 2 Materials and Methods  
Study Area: This study focuses on a region from Bumbesti-Jiu in Gorj County to the entrance of Petrosani in Hunedoara County, crossing the Southern Carpathians. This area (Figure 1) includes National Road 66, a railway network, and the Jiu River, making it crucial for understanding rockfall and flood hazards. Data Collection:  
Sentinel-1 Radar Imagery: For flood mapping, we used GRD-type Sentinel-1 radar images obtained from the Copernicus Open Access Hub. These images were processed with ESA SNAP software version 10.0.0 to generate interferograms necessary for distinguishing flooded areas from permanent water bodies ([PERSON] et al. [3]). The processing steps included subset creation, data calibration, terrain correction, and RGB composite generation. The composite was produced by merging pre-flood and post-flood images, with the pre  
Figure 1: Geographical location of the study area. -flood image used for the red channel and the post-flood image for the blue and green channels, enhancing flood detection (Figure 2). As can be seen in Figure 2, the SNAP software workflow for flood detection starts with the Sentinel-1 GRD product (left). The data are then calibrated and terrain correction is applied (second in grayscale). An RGB composite is created to differentiate the flood-affected area (the next two images). Finally, permanent water bodies are highlighted in black (right). Rockfall Detection: For rockfall analysis, we used SLC-type Sentinel-1 radar images ([PERSON] et al. [4]; [PERSON] et al. [5]; [PERSON], [PERSON] et al. [6]). Two images, taken before and after the event, were co-registered, interferometrically processed to capture ground deformation patterns, and geocoded to assign precise geographic coordinates (Figure 3). As can be seen in Figure 3, the SNAP software workflow for rockfall detection starts with the Sentinel-1 SLC product (left). The images are then co-registered, interferometrically processed, and geocoded (middle). Finally, an RGB composite is applied to differentiate the rockfall-affected area (right). Supplementary Data:  
Digital Elevation Models (DEMs): 10 m resolution DEMs provided detailed topographic data essential for identifying rockfall-prone areas ([PERSON], [PERSON] et al. [6]; [PERSON] et al. [7]; [PERSON], [PERSON] et al. [8]). CORINE Land Cover 2018: This dataset was used to analyze land surface characteristics. Precipitation Data: These were used to assess the impact of rainfall on slope stability. Figure 2: Sentinel 1 GRD images of study area from descending orbit (left, 20 January 2023), from ascending orbit (middle, 20 January 2023), RGB interferogram and processing software workflow. Proximity to Streams: This was considered to evaluate flood susceptibility. Data Integration and Analysis:  
ArcGIS Pro: All data layers were integrated using ArcGIS Pro version 3.0.1. Weighted overlays were applied to generate susceptibility maps for rockfalls and floods ([PERSON] et al. [9]; [PERSON], [PERSON] et al. [6]). These maps visually represent hazard-prone areas, aiding in risk mitigation and disaster preparedness. Artificial Intelligence for Traffic Detection:  
YOLO Models: Pretrained YOLO models were used for traffic detection, analyzing videos captured along National Road 66 to extract data on traffic patterns and congestion. Additionally, a custom YOLO model was trained to detect rockfalls from news images ([PERSON], [PERSON] et al. [8]; [PERSON], [PERSON] et al. [6]). Overview of the ArcGIS-YOLO Interface:  
The seamless integration of YOLO (You Only Look Once), a state-of-the-art real-time object detection system, with ArcGIS PRO, leading geographic information system (GIS) software version 3.0.1, plays a crucial role in hazard detection and infrastructure impact assessment. The following is a detailed explanation of this interface:  
Data Preprocessing and Image Annotation:  
Image Annotation: The process begins with annotating images for training the YOLO model.