## 1 Introduction  
The rapid urbanization of cities necessitates the availability of updated information regarding various urban features that can assist urban planning tasks, such as predicting population growth, analyzing park space, surveying the water supply, identifying transportation patterns, recognizing food supply demands, analyzing the impact of land use, and others. Counting and geotagging the number of particular features in a given urban area or city is crucial when making decisions for government policies and administration. Urban planning officials and stakeholders often lack updated information regarding such features and that can potentially lead to wrong decisions. The traditional methods used for counting and geotagging features such as trees, trash bins, street lamps, retail shops, and others, in most cases include manual inspection and consequently are time-consuming, costly, and labor-intensive. Therefore, there is an increasing need for an automated approach to replace conventional visual inspection. Recently, following the advent of deep learning (DL) and the availability of large datasets, many new models and approaches have emerged. These new methods have proven promising to revolutionize the way particular problems are tackled. Related literature keeps expanding with research studies that use deep learning to achieve better results. Specifically, computer vision (CV) is a domain that showcases substantial progress in recent years and a lot of research effort is directed toward CV applications. convolutional neural network (CNN), one of the basic building blocks of CV models, outperform many of their traditional machine learning (ML) counterparts. Consequently, CV tasks, such as object detection, object tracking, and semantic or instance segmentation have gained significant interest. The field has reached a state where powerful models and datasets are publicly available and even used for commercial and business applications, such as self-driving cars [1] and healthcare [2]. In the digital era, many urban research studies are trying to integrate methods that quickly process and analyze the complexity of urban dynamics and fast-paced surrounding changes. Machine learning and deep learning approaches are suitable tools for complex task automation for various urban elements [3]. For example, many works incorporate DL models to overcome specific urban problems, such as waste management [4], urban environment quality [5], structure damage detection [6], traffic prediction [7], and others. In this work, we present StreetScouting, a platform that utilizes powerful deep learning and computer vision techniques to automatically detect and extract urban features in cities. StreetScouting is a fully featured platform and its main task is to process GPS and video data of a particular region and output visualizations and data regarding various objects and urban features. Objects, such as trees, trash and recycle bins, street light poles and lamps, vehicles, traffic lights, traffic signs, and other features, such as the width of the road or the existence of sidewalks are some of the features that can be detected and localized by the platform. To achieve this, StreetScouting incorporates many state-of-the-art computer vision approaches for object detection, semantic segmentation, multiple object tracking, depth estimation, and others. Ultimately, StreetScouting aspires to be an extensible platform that can automatically capture and extract multiple visual features from urban streets and progressively eliminate the need for visual inspection. StreetScouting is a web application that provides an organized and easy-to-use environment that can be utilized by urban planning administrators and policymakers, municipal offices, geoinformatics companies, and other commercial organizations. StreetScouting uses street data as input and generates geotagged feature information that can be further utilized according to user needs.