A Comparative Evaluation of Urban Fabric Detection Techniques Based on Mobile Traffic Data

International audienceMobile traffic data has been recently used to characterize the urban environment in terms of urban fabric profiles. While showing promising results, the existing urban fabric detection solutions are built without a clear understanding of the detection process chain. In this paper, we distinguish and analyze the different steps common to all urban profiling techniques. By evaluating the impact of each step of the process, we are able to propose a new solution that outperforms the state of the art techniques. Our approach uses the weekly periodicity of human activities, as well as a median-based filtering technique, resulting in a better clustering in terms of both coverage and entropy, as shown by results obtained on two large scale mobile traffic datasets covering the urban areas of Milan and Turin, in Italy

Urban morphology analysis by remote sensing and gis technique, case study: Georgetown, Penang

This paper was analysed the potential of applications of satellite remote sensing to urban planning research in urban morphology. Urban morphology is the study of the form of human settlements and the process of their formation and transformation. It is an approach in designing urban form that considers both physical and spatial components of the urban structure. The study conducted in Georgetown, Penang purposely main to identify the evolution of urban morphology and the land use expansion. In addition, Penang is well known for its heritage character, especially in the city of Georgetown with more than 200 years of urban history. Four series of temporal satellite SPOT 5 J on year 2004, 2007, 2009 and 2014 have been used in detecting an expansion of land use development aided by ERDAS IMAGINE 2014. Three types of land uses have been classified namely build-up areas, un-built and water bodies show a good accuracy with achieved above 85%. The result shows the built-up area significantly increased due to the rapid development in urban areas. Simultaneously, this study provides an understanding and strengthening a relation between urban planning and remote sensing applications in creating sustainable and resilience of the city and future societies as well

A Tale of Ten Cities: Characterizing Signatures of Mobile Traffic in Urban Areas

International audienceUrban landscapes present a variety of socio-topological environments that are associated to diverse human activities. As the latter affect the way individuals connect with each other, a bound exists between the urban tissue and the mobile communication demand. In this paper, we investigate the heterogeneous patterns emerging in the mobile communication activity recorded within metropolitan regions. To that end, we introduce an original technique to identify classes of mobile traffic signatures that are distinctive of different urban fabrics. Our proposed technique outperforms previous approaches when confronted to ground-truth information, and allows characterizing the mobile demand in greater detail than that attained in the literature to date. We apply our technique to extensive real-world data collected by major mobile operators in ten cities. Results unveil the diversity of baseline communication activities across countries, but also evidence the existence of a number of mobile traffic signatures that are common to all studied areas and specific to particular land uses

Towards Sustainable Urban Futures: Exploring Environmental Initiatives in Smart Cities

Environmentally sustainable smart cities have emerged as a promising approach to address the challenges of urbanization while promoting sustainable development and enhancing residents' quality of life. This research article presents the key findings of a comprehensive study that explores the various aspects and initiatives found in environmentally sustainable smart cities.Renewable energy plays a pivotal role in these cities, with a strong emphasis on harnessing solar, wind, and geothermal power. Investments in clean energy infrastructure, such as solar panels, wind farms, and geothermal plants, significantly reduce reliance on fossil fuels and contribute to lower carbon emissions.Energy efficiency is another critical aspect of sustainable smart cities. These cities prioritize the use of smart grids for optimized energy distribution, smart meters for real-time energy monitoring and control, and energy-efficient buildings equipped with insulation, lighting, and HVAC systems that minimize energy consumption.Smart transportation is a key initiative in environmentally sustainable smart cities, focusing on reducing traffic congestion and air pollution. Electric vehicles (EVs) are promoted, accompanied by the development of charging infrastructure. Intelligent transportation systems aid in effective traffic management, while active transportation modes such as cycling, walking, and public transportation are encouraged.Efficient waste management systems are implemented to minimize landfill waste and promote recycling and composting. Smart waste bins equipped with sensors optimize waste collection routes, reduce littering, and provide real-time data on fill levels, aiding in effective waste management.Water management strategies are prioritized to conserve this precious resource. Smart water meters monitor consumption patterns, rainwater harvesting systems are implemented, water-efficient practices are promoted in buildings, and advanced leak detection technologies minimize water loss.Green spaces and biodiversity conservation are fundamental in environmentally sustainable smart cities. By integrating parks, gardens, rooftop greenery, and urban forests, these cities enhance residents' well-being, improve air quality, and provide habitats for wildlife, thus promoting biodiversity.Data analytics and the Internet of Things (IoT) play a crucial role in monitoring and optimizing various city systems. Real-time data collection and analysis enable effective management of energy usage, traffic flow, waste management, and other infrastructure, facilitating informed decision-making and resource allocation.Citizen engagement is fostered in environmentally sustainable smart cities. Platforms for citizen participation enable residents to provide feedback, report issues, and actively contribute to decision-making processes related to urban planning, energy conservation, waste management, and other sustainability initiatives.The implementation of these strategies in environmentally sustainable smart cities aims to reduce carbon footprints, enhance resource efficiency, improve air and water quality, and create healthier and more livable urban environments. By embracing technology, innovation, and citizen engagement, these cities pave the way for a sustainable and resilient future

Fusing GPS Probe and Mobile Phone Data for Enhanced Land-Use Detection

International audienceProfiling the diversity of land use in modern cities by mining data related to human mobility represents a challenging problem in urban planning, transportation and smart city management. Previous work on mobile phone data (i.e., Call Detail Records) has shown the existence of strong correlations between the urban tissue and the associated mobile communication demand. Similarly, GPS traces of vehicles convey information on transportation demand and human activities that can be related to the land use of the neighborhood where they take place. In this paper, we investigate the land use patterns that emerge when studying simultaneously GPS traces of probe vehicles and mobile phone data collected by network providers. To this end, we extend previous definitions of mobile phone traffic signatures for land use detection, so as to incorporate additional information on human presence and mobility conveyed by GPS traces of vehicles. Leveraging these extended signatures, we exploit an unsupervised learning technique to identify classes of signatures that are distinctive of different land use. We apply our technique to real-world data collected in French and Italian cities. Results unveil the existence of signatures that are common to all studied areas and specific to particular land uses. The combined use of mobile phone data and GPS traces outperforms previous approaches when confronted to ground-truth information, and allows characterizing land use in greater detail than in the literature to date

Software Defined Multi-Spectral Imaging for Arctic Sensor Networks

Availability of off-the-shelf infrared sensors combined with high definition visible cameras has made possible the construction of a Software Defined Multi-Spectral Imager (SDMSI) combining long-wave, near-infrared and visible imaging. The SDMSI requires a real-time embedded processor to fuse images and to create real-time depth maps for opportunistic uplink in sensor networks. Researchers at Embry Riddle Aeronautical University working with University of Alaska Anchorage at the Arctic Domain Awareness Center and the University of Colorado Boulder have built several versions of a low-cost drop-in-place SDMSI to test alternatives for power efficient image fusion. The SDMSI is intended for use in field applications including marine security, search and rescue operations and environmental surveys in the Arctic region. Based on Arctic marine sensor network mission goals, the team has designed the SDMSI to include features to rank images based on saliency and to provide on camera fusion and depth mapping. A major challenge has been the design of the camera computing system to operate within a 10 to 20 Watt power budget. This paper presents a power analysis of three options: 1) multi-core, 2) field programmable gate array with multi-core, and 3) graphics processing units with multi-core. For each test, power consumed for common fusion workloads has been measured at a range of frame rates and resolutions. Detailed analyses from our power efficiency comparison for workloads specific to stereo depth mapping and sensor fusion are summarized. Preliminary mission feasibility results from testing with off-the-shelf long-wave infrared and visible cameras in Alaska and Arizona are also summarized to demonstrate the value of the SDMSI for applications such as ice tracking, ocean color, soil moisture, animal and marine vessel detection and tracking. The goal is to select the most power efficient solution for the SDMSI for use on UAVs (Unoccupied Aerial Vehicles) and other drop-in-place installations in the Arctic. The prototype selected will be field tested in Alaska in the summer of 2016