Abstract: A system may be configured to conflate vectorized source data. Some embodiments may: obtain first data from a first source and second data from a second source; determine a first polygon that encloses all features of the first data and a second polygon that encloses all features of the second data; determine a larger polygon that encloses the first and second polygons; divide the larger polygon into first tiles; extract, from each of the first tiles overlaying the first data and from the each tile overlaying the second data, a first set of features and a second set of features, respectively; and identify, based on a computed disagreement level satisfying a set of criteria, each of one or more of the tiles. A set of identified tiles or all of the tiles may then be displayed, including shaded indicators overlaying features of respective portions of the first and second data.

Abstract: A system may be configured to collect geospatial features (in vector form) such that a software application is operable to edit an object represented by at least one vector. Some embodiments may: generate, via a trained machine learning model, a pixel map based on an aerial or satellite image; convert the pixel map into vector form; and store the vectors. This conversion may include a raster phase and a vector phase. A system may be configured to obtain another image, generate another pixel map based on the other image, convert the other pixel map into vector form, and compare the vectors to identify changes between the images. Some implementations may cause identification, based on a similarity with converted vectors, of a more trustworthy set of vectors for subsequent data source conflation.

Abstract: A system may be configured to collect geospatial features (in vector form) such that a software application is operable to edit an object represented by at least one vector. Some embodiments may: generate, via a trained machine learning model, a pixel map based on an aerial or satellite image; convert the pixel map into vector form; and store the vectors. This conversion may include a raster phase and a vector phase. A system may be configured to obtain another image, generate another pixel map based on the other image, convert the other pixel map into vector form, and compare the vectors to identify changes between the images. Some implementations may cause identification, based on a similarity with converted vectors, of a more trustworthy set of vectors for subsequent data source conflation.

Abstract: A system may be configured to conflate vectorized source data. Some embodiments may: obtain first data from a first source and second data from a second source; determine a first polygon that encloses all features of the first data and a second polygon that encloses all features of the second data; determine a larger polygon that encloses the first and second polygons; divide the larger polygon into first tiles; extract, from each of the first tiles overlaying the first data and from the each tile overlaying the second data, a first set of features and a second set of features, respectively; and identify, based on a computed disagreement level satisfying a set of criteria, each of one or more of the tiles. A set of identified tiles or all of the tiles may then be displayed, including shaded indicators overlaying features of respective portions of the first and second data.

Abstract: A system may be configured to collect geospatial features (in vector form) such that a software application is operable to edit an object represented by at least one vector. Some embodiments may: generate, via a trained machine learning model, a pixel map based on an aerial or satellite image; convert the pixel map into vector form; and store the vectors. This conversion may include a raster phase and a vector phase. A system may be configured to obtain another image, generate another pixel map based on the other image, convert the other pixel map into vector form, and compare the vectors to identify changes between the images. Some implementations may cause identification, based on a similarity with converted vectors, of a more trustworthy set of vectors for subsequent data source conflation.