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3 results for “Mapillary”
Mapillary Annotated Street Level Images
<p>This dataset contains street-level images from the Mapillary platform, for the year of 2017, in the <a href="https://zenodo.org/api/files/e945988e-2c3d-4cc6-9cd2-ee7761a2f15a/mapillary.zip?versionId=86c5f856-324c-45a6-aa9d-03a0f661a0b2">mapillary.zip</a> file. These images have been matched with crop type labels from the freely available Land Parcel Identification System (LPIS) of the Netherlands. The name of each .jpf file corresponds to a unique identifier.</p> <p>Supported by the CALLISTO (No. v) project, which has been funded by EU Horizon 2020 programs.</p>
Mapillary POI-Neighborhood Street-Level Images (MPOINSLI)
<p><em><strong>Dataset Name:</strong></em> MPOINSLI Mapillary POI-Neighborhood Street-Level Images </p> <blockquote> <p>This is a repository of Mapillary street-view images of New York City that include any portion of POIs in their field of view. The repository is the outcome of a paper, the abstract of which is provided below. Please use the below citatin for using this dataset:</p> <p> </p> </blockquote> <p><strong>Citation: </strong></p> <p>N. Zarbakhsh and G. McArdle, "Points-of-Interest from Mapillary Street-level Imagery: A Dataset For Neighborhood Analytics," 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW), Anaheim, CA, USA, 2023, pp. 154-161, doi: 10.1109/ICDEW58674.2023.00030.</p> <p><strong>Abstract: </strong></p> <p>The Sustainable Development Goals of the United Nations promote sustainable urban development to make cities more economically and socially liveable. Points of Interest (POIs) such as commercial properties and healthcare facilities are significant markers for these goals. Street-view images are becoming increasingly important for capturing cities' streetscapes. Existing studies provide city-level images, while there are few studies that provide images in the vicinity of certain POIs. Therefore, this paper develops a framework for filtering images so that a portion of a given POI is visible in their field of view (FOV). We contribute with Mapillary POI-Neighborhood Street-Level Images (MPOINSLI) dataset, a large street-view image of POIs and their neighborhood in New York City. First, all the images within a 35-meter radius of certain POIs are filtered. Then, the intersection technique is utilized to determine if the cameras' FOV triangular polygons intersect the POIs' polygons. Using 11,126 POIs from SafeGraph's Geometry and Place datasets in conjunction with 875,592 Mapillary images, we demonstrate the effectiveness of our approach. MPOINSLI contains 167,743 Mapillary street-view images of 6,732 unique POIs, defined by the standard identifiers (Placekeys) which are further classified into 23 general functionalities categories (top-categories) and 67 more specific categories (sub-categories) of the POIs. MPOINSLI provides an open-source repository that contains metadata such as raw and post-processed camera-related parameters, the Harvesian distance between the camera and the POI's coordinates, and the intersection area. MPOINSLI could provide promising future applications for both smart cities and computer vision, including scene recognition across POI neighborhoods and fine-grained land-use classification.</p>
Mapillary Vistas Geographic MetaData
<p>Latitude and Longitude information of the Mapillary Vistas training and validation set. </p>
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