For example, here is a GeoJson file with New York as a reference point. This file format has been chosen due to its compactness relative to other XML formats, as well as its easy readability. Microsoft’s building footprint and new roads data is easily accessible in the GeoJson format, which is commonly used to encode a range of different geographic data. Once identified areas are mapped and verified, humanitarian organizations can act quickly in times of need. The Humanitarian OpenStreetMap Team is effectively crowdsourcing the identification of potentially vulnerable areas with the use of road network data. One of the most popular applications of our Microsoft Maps AI data, both these new roads and building footprints, can been seen in OpenStreetMap. Having an accurate map of rural and urban roads is a necessary condition for effective long-term planning and can save precious time and resources that would otherwise have been used for surveying access options to remote areas. This set of road network data is useful for a range of different applications. As Bing Maps Imagery is a composite of multiple sources it is difficult to know the exact dates for individual pieces of data but the Microsoft Maps AI Team is now connected to the direct imagery update pipeline for Bing Maps so will continue to refine and update this dataset as new imagery is acquired. The vintage of the roads depends on the vintage of the underlying imagery. After classifiers filtered out potentially bad roads, the precision was remeasured and made sure that it is 95% before releasing results. The “Missing” OSM Data went through a final classifier to ensure that the precision is at least 95%. This focused on Pixel metric measures for the performance of the Convolutional Neural Network and APLS metric (Average Path Length Similarity) to measure overall road connectivity after the road geometry generation stage. The Microsoft Maps AI Team measured intermediate stage metrics to track performance of the models. Classification - A classifier to filter out low-confidence roads and predict a road type.
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