Florin Teodorescu

A new approach to geocoding: BingGC

Real-time geocoders help users find precise locations in online mapping systems. Geocoding unstructured queries can be difficult, as users may describe map locations by referencing several spatially co-located entities (e.g., a business near a street intersection). Serving these queries is important as it provides new capabilities and allows for expanding in markets with less structured postal systems. Traditionally, this problem poses significant difficulties for online systems where latency constraints prevent exhaustive join-based algorithms. Previous work in this area involved natural language processing to segment queries based on known rules, or purely spatial approaches that are difficult to maintain and may have high latency. In this paper, we present a new approach to geocoding - BingGC - that makes fulfillment of extremely diverse geocoding queries possible via a combination of traditional web search technologies and a novel algorithm that uses textual search and spatial joins to quickly find results. It allows resolution of up to s spatially co-located entities in a single query with no pre-computation or rule-based matching. We provide experimental analysis of our system compared against leading online geocoders.

LiveMaps: Converting Map Images into Interactive Maps

Image search is a popular application on web search engines. Issuing a location-related query in image search engines often returns multiple images of maps among the top ranked results. Traditionally, clicking on such images either opens the image in a new browser tab or takes users to a web page containing the image. However, finding the area of intent on an interactive web map is a manual process. In this paper, we describe a novel system, LiveMaps, for analyzing and retrieving an appropriate map viewport for a given image of a map. This allows annotation of images of maps returned by image search engines, allowing users to directly open a link to an interactive map centered on the location of interest. LiveMaps works in several stages. It first checks whether the input image represents a map. If yes, then the system attempts to identify what geographical area this map image represents. In the process, we use textual as well as visual information extracted from the image. Finally, we construct an interactive map object capturing the geographical area inferred for the image. Evaluation results on a dataset of high ranked location images indicate our system constructs very precise map representations also achieving good levels of coverage.

LiveMaps: Learning Geo-Intent from Images of Maps on a Large Scale

Image search is a popular application on web search engines. Issuing a location-related query on an image search engine often returns multiple images of maps among the top ranked results. Traditionally, clicking on such images either opens the image in a new browser tab or takes users to a web page containing the image. However, finding the area of intent on an interactive web map (e.g., Bing Maps) is a manual process. In this paper, we describe a novel system, LiveMaps, for analyzing and retrieving an appropriate map viewport for a given image of a map. This provides annotation of images of maps returned by image search engines, allowing users to directly open a link to an interactive map centered on the location of interest. LiveMaps works in several stages. It first checks whether the input image represents a map. If yes, then the system attempts to identify what geographical area this map image represents. In the process, we use textual as well as visual information extracted from the image. Finally, we construct an interactive map object capturing the geographical area inferred for the image. Evaluation results on a dataset of labeled map images indicate our system constructs precise map representations while also achieving good levels of coverage.