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Collecting and validating geospatial data for every commercial location in the USA and Canada is not an easy task. It requires aggregation of data from multiple sources and formats. This data then needs to be validated and decisions need to be made about which data sources represent the truth in the case of conflicting data. Safegraph does this weighing datasets based on certain criteria and using a voting system.
Data is being scraped and curated from multiple different sources but it some cases it is also necessary to create data. Think of the use case of a shopping center. If you think of the shopping center as being a collection of geometries where the entire shopping center is the parent geometry and the individuals business in the shopping center are child objects. In this situation, Safegraph has had to digitize entire shopping centers manually in order to properly represent the parent/ child geometry relationships.
This episode is sponsored by HiveMapper
A platform that takes video and creates 3D mapping layers based on that data. The video can be from avariety of different sensors, does not need to be vertically looking down on the geography and each 3D output is georeferenced!
To put it simply, point clouds are a collection of XYZ points that represent some real world object of nearly any scale.They can be generated in a few ways. As geospatial scientists, we mostly work with LAS/LAZ data collected by aerial LiDAR (light detection and ranging) scanners at varying scales, from landscapes, down to project sites. We may also derive point clouds from highly detailed orthoimagery of an area, such as from the products of a drone flight.
As a data scientist, you don’t just go in and solve problems. You make recommendations to multi-faceted issues so that you get a fantastic model in the end. You’ll also be advocating a better use and understanding of the data while you do that.