Smart City

Crowdsourcing based Smart City Service Framework

Figure 1. Crowdsourcing based Road Environment Report & Local Event Map Service Framework

    As the various and many mobile devices come into wide use, it becomes possible to improve the quality of life for citizens by providing the more smart service through being reported the local information of each mobile and using them. In the crowdsourcing based road environment report & local event map service framework, the illegal parking re-identification to solve the traffic congestion smartly and the local event map service that can provide local information to citizens in real time is provided.

1. Semantic aware Road Environment Data Re-identification

    – To verify that images from two different view point is spatially identical, analyze the image in three level(Point-level, Scene-level, Object-level)

    – In order to match the texture of images, extract the point-level feature by using Scale-Invariant Feature Transform(SIFT) algorithm.

    – For the scene-level and object-level feature matching, we will develop deep learning model to analyze the semantic information finally.

    – By analyzing the point-level matching similarity and semantic information, comprehensive judgment for the illegal parking re-identification is conducted.

2. Local Event Map (LEM) Service

    – Development of location-based local information sharing service using crowd sourcing citizen participation report on road environment information such as illegal parking, pot hole, road kill, road obstacle, and city life event information

    – Design hierarchical structure of four layered event maps consisting of low-level base map and high-level cultural events (Layer 1: Base map, Layer 2: Road traffic information layer, Layer 3: Building information layer, 4th layer: Cultural events layer)

    – OSM, PosgGIS DB, and PostGIS to collect geo metadata and GeoServer creates a base map by rendering the map using Mapnik. OpenLayers create multi-event layer map and service.

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