## 6 Discussion  
StreetScouting can be utilized by urban planning administrators and policymakers, municipal offices, geoinformatics companies, and other commercial organizations, for applications related to public health, real estate, traffic detection, waste management, and others. More specifically, urban planning officials can automatically check the density of the trees in particular regions to plan the creation of parks or new green areas. Municipal administrators can collect data regarding the number of street lights that are available. Information regarding the number of retail shops can be important for a real estate application. The number and position of recycling or trash bins can support waste management campaigns. StreetScouting generated data can be further utilized for statistical analysis and geographic information systems. The motivation for StreetScouting started in the context of the GRUBLES project. The goal of the project is to build statistical models that can infer socioeconomic indicators of a region by using information regarding the number and locations of urban features. For this use case, StreetScouting is configured to track specific urban features, such as trees, street lights, retail shops, recycling, and trash bins, vehicles, traffic signs, and traffic lights. Some of these features are selected based on the related literature. For example, trees are important for health in an urban environment [52], the detection of trash bins can enable efficient waste management activities [53], and the detection of vehicles is important for traffic management [54]. The rest of the features are selected from the user requirements process of the GRUBLES project. One example of the outcomes of GRUBLES can be seen in Figure 10. In the figure, the recycling bin coverage of a particular region is shown. This analysis uses the results of StreetScouting and other publicly available census data to calculate additional statistics, such as how many residents correspond to one recycle bin and locations that require more bins. It is easy to extend StreetScouting to other use cases. Any new user of the platform can collect data with the proposed data collection method and define the region of interest. Data collection should be performed with car speeds that are imposed within urban regions but in any case not higher than 90 km/h to avoid object tracking errors. There is no limitation on the size of the region coverage. Besides the already mentioned urban features, StreetScouting can be configured to detect other features as well, for example in rural areas. This process would require the creation of need object detection models using annotation and fine-tuning. Figure 9: Map visualization of the detected features from a sample video in the city of Thessaloniki. The generation of the StreetScouting results is asynchronous and the processing time increases linearly with the duration of input videos. The hardware requirements for StreetScouting include a typical server with at least 32 GB ram, a multi-core CPU, and a CUDA-enabled GPU. The average execution time of StreetScouting is equal to the input video duration. One limitation of StreetScouting is that the location estimation module cannot estimate precise location information. This limitation is not so crucial for the intended use of StreetScouting as it is more important for StreetScouting to correctly detect the object and provide a rough estimation of the location. StreetScouting is not proposed for applications that require accurate location information. Moreover, as mentioned in Section 3.1, StreetScouting currently assumes that the data collection route is not overlapping, meaning that precise routes are designed beforehand by specific personnel. For the updated version of the platform, we plan to exploit the GPS information of the detected objects in order to discard duplicate detected features from overlapping data collection routes.