Following on the theme of using Openstreet map to identify spatial context of building here’s the syntax to identify schools within a bounding box defined as your selected field of view. This identifies the building plot not all building types are accurately tagged in the open street database but schools coverage is distinct and appears to be relatively complete. This is excellent for identifying at scale the spatial context of schools anywhere in the world. Overpass uses its’ own query language that can be used to filter for specific attributes.

Overpass-turbo.eu

[out:json][timeout:25];
// Gather elements tagged as amenity=school within the current map view
(
  node["amenity"="school"]({{bbox}});
  way["amenity"="school"]({{bbox}});
  relation["amenity"="school"]({{bbox}});

);
// Output the result
out body;
>;
out skel qt;

Schools in Edinburgh

You can swap out the query to the following

[out:json][timeout:25];
(
  node["building"="school"]({{bbox}});
  way["building"="school"]({{bbox}});
  relation["building"="school"]({{bbox}});
);
out body;
>;
out skel qt;

Alternative syntax searching on the building rather than the area

The former query is probably better as the building syntax returns separate records for each building on a campus, which creates the illusion in summary statistics that there are more school organisations than actually exist . Schools for whatever reason seem to be one of the most accurate datasets within Openstreetmap and when altering the syntax to try and identifying polygons with the string “church” we get a null return for Edinburgh so there is a question of data quality consistency between categories. My experience has been is that providing you take this into consideration there are specific “categories” of information for which openstreetmap is the only available, free, practical and complete resource. I have found that within specific categories data quality appears consistent (complete or not so complete) but between categories consistency varies significantly.