## Data and Methods
### Object Location Estimation  
Providing information regarding detected objects should be accompanied by accurate GPS coordinates. The position of the objects is important for many GIS systems and geo-processing tools, especially when aiming to apply statistical modeling to specific regions of interest. For this task, we consider two different approaches, the first is focused on accuracy, and the second aims at low execution time. It is worth noting that location estimation in both approaches is still a rough estimation and is not suggested for applications that require precise location information.  
For the first approach, we employ a depth estimation method that follows the object detection and tracking procedure. Depth estimation is the task of measuring the distance of each pixel relative to the camera. For this task, we use DNET [44], which is a novel self-supervised monocular depth estimation pipeline that exploits densely connected hierarchical features to obtain more precise object-level depth inference, and uses dense geometrical constraints to eliminate the dependence on additional sensors or depth ground-truth to perform scale recovery, so that it is easier to transform relative to absolute depth. The data split of the KITTI [45] dataset by [PERSON] et al. [46] was used for training and evaluation purposes. DNET achieves 4.812 RMSE on the evaluation subset containing 697 test images.  
To estimate the location of an object with this approach, we first apply the object detection models to localize it in the frame (Figure 2a) and then we calculate the distance of the object from the camera by applying the depth estimation model (Figure 2b). Having the object's bounding box and the respective pixel-level absolute depth in meters, we calculate the median depth value for every object localization to compute the distance [47]. Knowing  
Figure 1: Object tracking example in consecutive frames.  
the object's distance \(d\) from the camera and the camera's GPS \(lat_{1},lon_{1}\) coordinates we can also infer the bearing \(\theta\) from the direction of the car and camera and the object localization in the frame. Let \(R\) be the radius of Earth and \(Ad\) be the angular distance i.e., \(d/R\) The resulting coordinates \(lat_{2},lon_{2}\) in radians are given from the following formulas:  
\[lat_{2}=\text{asin}(\sin lat_{1}*\cos Ad+\cos lat_{1}*\sin Ad*\cos\theta) \tag{1}\]  
\[lon_{2}=lon_{1}+\text{atan2}(\sin\theta*\sin Ad*\cos lat_{1},\cos Ad-\sin lat_{ 1}*\sin lat_{2}) \tag{2}\]  
This procedure can be applied iteratively in every frame the object appears and then average the calculations for the final estimation. We refer to this approach as _depth_ location estimation.  
One disadvantage of this approach is that it is slow. For applications that do not require such a fine-grained location estimation approach, we also provide a simpler heuristic approach. This approach is based on the assumption that the final detection of an object occurs when the object is very close to the camera. Although this might not always be true, generally is an acceptable rule of thumb. Based on this, the GPS coordinates of the camera, when the last detection of the object occurs, are used as the final position of the object. We refer to this approach as _fast_ location estimation. StreetScouting provides both location estimation methods. The first is preferred when accurate location estimation is required and the latter is used in scenarios that require faster processing sacrificing the location accuracy.