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ItemTechnology pillars for digital transformation of cities based on open software architecture for end2end data streaming( 2022-01-04)The goal of the paper is to expose the pillars of urban digital infrastructure and their role in the development of smart and data driven applications in the e2e model. Today, no one contests the need for a general-purpose infrastructure in the city that improves the quality of life with respect to various types of socio-economic activity of residents. However, the digital aspect of fostering this kind of activity is linked to a very dynamic and ubiquitous element of technology, and the quality and stability of solutions based on advanced technology is a key parameter of the quality of life and development that a city should provide to its residents. This pace of technology development means that our environment (including cities) are subject to a process of continuous digital transformation. Starting from examples of such transformation and top-trends in technology development, we focus on transformation pillars that can ensure sustainable growth of innovation supply in a city. We give three examples of platforms designed for City of Poznan. We conclude that a key factor benefiting this multidimensional transformation process is the provision of an open, data-transmission and processing infrastructure with the support of which all smart city stakeholders can pursue their social, scientific, economic, or political goals in a sustainable manner.
ItemPorting Computer Vision Models to the Edge for Smart City Applications: Enabling Autonomous Vision-Based Power Line Inspection at the Smart Grid Edge for Unmanned Aerial Vehicles (UAVs)( 2022-01-04)Smart grid infrastructure must be monitored and inspected - especially when subject to harsh operating conditions in extreme, remote environments such as the highlands of Iceland. Current methods for monitoring such critical infrastructure includes manual inspection, static video analysis (where connectivity is available) and unmanned aerial vehicle (UAV) inspection. UAVs offer certain inspection efficiencies; however, challenges persist given the time and UAV operator skill required. Collaborating with Landsnet, the Icelandic smart grid operator, we apply convolutional neural networks for image processing to detect smart grid transmission infrastructure and modify the resulting computer vision (CV) model to function on the edge of a UAV. In doing so, we overcome significant edge processing barriers. Our real-time CV model delivers decision insight on the UAV edge and enables autonomous flight path planning for use in smart grid inspection. Our approach is transferable to other smart city applications that could benefit from edge-based monitoring and inspection.
ItemDeep Learning Based Parking Vacancy Detection for Smart Cities( 2022-01-04)Parking shortage is a major problem in modern cities. Drivers cruising in search of a parking space directly translate into frustration, traffic congestion, and excessive carbon emission. We introduce a simple and effective deep learning-based parking space notification (PSN) system to inform drivers of new parking availabilities and re-occupancy of the freed spaces. Our system is particularly designed to target areas with severe parking shortages (i.e., nearly all parking spaces are occupied), a situation that allows us to convert the problem of detecting parking vacancies into recognizing vehicles leaving from their stationary positions. Our PSN system capitalizes on a calibrated Mask R-CNN model and a unique adaptation of the IoU concept to track the changes of vehicle positions in a video stream. We evaluated PSN using videos from a CCTV camera installed at a private parking lot and publicly available YouTube videos. The PSN system successfully captured all new parking vacancies arising from leaving vehicles with no false positive detections. Prompt notification messages were sent to users via cloud messaging services.