DeepNav: Learning to Navigate Large Cities

We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for navigating large cities using locally visible street-view images. The DeepNav agent learns to reach its destination quickly by making the correct navigation decisions at intersections. We collect a large-scale dataset of street-view images organized in a graph where nodes are connected by roads. This dataset contains 10 city graphs and more than 1 million street-view images. We propose 3 supervised learning approaches for the navigation task and show how A* search in the city graph can be used to generate supervision for the learning. Our annotation process is fully automated using publicly available mapping services and requires no human input. We evaluate the proposed DeepNav models on 4 held-out cities for navigating to 5 different types of destinations. Our algorithms outperform previous work that uses hand-crafted features and Support Vector Regression (SVR)[19].Comment: CVPR 2017 camera ready versio

Smart City Development with Urban Transfer Learning

Nowadays, the smart city development levels of different cities are still unbalanced. For a large number of cities which just started development, the governments will face a critical cold-start problem: 'how to develop a new smart city service with limited data?'. To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm. This article investigates the common process of urban transfer learning, aiming to provide city planners and relevant practitioners with guidelines on how to apply this novel learning paradigm. Our guidelines include common transfer strategies to take, general steps to follow, and case studies in public safety, transportation management, etc. We also summarize a few research opportunities and expect this article can attract more researchers to study urban transfer learning

Learning cities 2020

This article provides a brief overview of historic work in the field of Learning City development. It then proceeds to highlight two contemporary strands of work. The first is the initiative of UNESCO’s Institute for Lifelong Learning (UIL) in establishing the International Platform of Learning Cities. The second is the work of the PASCAL Observatory, currently manifested in the Learning Cities 2020 programme

A Place to Grow and Learn: A Citywide Approach to Building and Sustaining Out-of-School Time Learning Opportunities

Drawing on lessons from five cities, offers "action elements" to help cities achieve widespread, sustainable improvements in out-of-school learning opportunities so that many more children benefit

Reinforcement machine learning for predictive analytics in smart cities

The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( QC ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework

Nursery Cities: Urban Diversity, Process Innovation and the Life-Cycle of Products

A simple model of process innovation is proposed, where firms learn about their ideal production process by making prototypes. We build around this a dynamic general equilibrium model, and derive conditions under which diversified and specialised cities coexist. New products are developed in diversified cities, trying processes borrowed from different activities. On finding their ideal process, firms switch to mass-production and relocate to specialised cities with lower costs. When in equilibrium, this configuration welfare-dominates those with only diversified or only specialised cities. We find strong evidence of this relocation pattern in establishment relocations across French employment areas 1993û1996.Cities, diversity, specialisation, innovation, learning, life-cycle

Transfer Learning for Thermal Comfort Prediction in Multiple Cities

HVAC (Heating, Ventilation and Air Conditioning) system is an important part of a building, which constitutes up to 40% of building energy usage. The main purpose of HVAC, maintaining appropriate thermal comfort, is crucial for the best utilisation of energy usage. Besides, thermal comfort is also crucial for well-being, health, and work productivity. Recently, data-driven thermal comfort models have got better performance than traditional knowledge-based methods (e.g. Predicted Mean Vote Model). An accurate thermal comfort model requires a large amount of self-reported thermal comfort data from indoor occupants which undoubtedly remains a challenge for researchers. In this research, we aim to tackle this data-shortage problem and boost the performance of thermal comfort prediction. We utilise sensor data from multiple cities in the same climate zone to learn thermal comfort patterns. We present a transfer learning based multilayer perceptron model from the same climate zone (TL-MLP-C*) for accurate thermal comfort prediction. Extensive experimental results on ASHRAE RP-884, the Scales Project and Medium US Office datasets show that the performance of the proposed TL-MLP-C* exceeds the state-of-the-art methods in accuracy, precision and F1-score

Midcourse Corrections to a Major Initiative: A Report on The James Irvine Foundation's CORAL Experience

Draws lessons from the reorientation of the Communities Organizing Resources to Advance Learning (CORAL) Initiative, a $60 million initiative aimed at improving educational achievement in low-performing schools in five California cities

Teaching Cultural Heritage using Mobile Augmented Reality

open2noThe relationship between augmented reality, mobile learning, gamification and non-formal education methods provide a great potential. The AR-CIMUVE Augmented Reality for the Walled Cities of the Veneto is an original project in collaboration with Italia Nostra and other associations which deal with transmitting our cultural heritage and which teach primary and middle school children the cultural and historical importance of the Veneto’s and the surrounding territories’ walled cities. In this learning experience students will explore how our environment has developed across the ages using the mobile devices with the technical back-up of the AR App. This will allow them to see maps, examine data, 3D models and will enable them to judge and improve their skills. From a pedagogical and educational point of view the emphasis is on a constructivist social-cultural approach which helps students to become active citizens more aware of their historical identity.openPetrucco, Corrado; Agostini, DanielePetrucco, Corrado; Agostini, Daniel