{'fraction_non_alphanumeric': 0.023712737127371274, 'fraction_numerical': 0.009485094850948509, 'mean_word_length': 4.83794466403162, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A computational framework for ontologically storing and analyzing very large overhead image sets

We describe a computational approach to remote sensing image analysis that addresses many of the classic problems associated with storage, search, and query. This process starts by automatically annotating the fundamental objects in the image data set that will be used as a basis for an ontology, including both the objects (such as building, road, water, etc.) and their spatial and temporal relationships (is within 100 m of, is surrounded by, has changed in the past year, etc.). Data sets that can include multiple time slices of the same area are then processed using automated tools that reduce the images to the objects and relationships defined in an ontology based on the primitive objects, and this representation is stored in a geospatial-temporal semantic graph. Image searches are then defined in terms of the ontology (e.g. find a building greater than 103 m2 that borders a body of water), and the graph is searched for such relationships. This approach also enables the incorporation of non-image data that is related to the ontology. We demonstrate through an initial implementation of the entire system on large data sets (109 -- 1011 pixels) that this system is robust against variations in different image collection parameters, provides a way for analysts to query data sets in a more natural way, and can greatly reduce the memory footprint of the search.