Abstract: Methods and systems for determining the height of structures based on imagery of the structures and associated two-dimensional vector data are provided. An example method involves projecting two-dimensional vector data outlining a roof of a structure into images of the structure captured from different perspectives and feature matching the vector data across the imagery to determine a best-matching three-dimensional position for the roof situated at the height of the structure.
Type:
Application
Filed:
April 7, 2025
Publication date:
July 24, 2025
Applicant:
Ecopia Tech Corporation
Inventors:
Yuanming SHU, Shuo TAN, Zihao CHEN, Ruijie DENG
Abstract: Methods and systems for generating artificial parcel data are provided. An example method involves accessing geospatial imagery depicting one or more buildings and surrounding areas, applying a machine learning model to the geospatial imagery to generate artificial parcel data in a form of a distance-transform raster map that represents a legal land parcel for each of the one or more buildings, and converting the distance-transform raster map into a vector map containing one or more polygons that represent the boundaries of each of the legal land parcels.
Abstract: Methods and systems for determining the height of structures based on imagery of the structures and associated two-dimensional vector data are provided. An example method involves projecting two-dimensional vector data outlining a roof of a structure into images of the structure captured from different perspectives and feature matching the vector data across the imagery to determine a best-matching three-dimensional position for the roof situated at the height of the structure.
Type:
Application
Filed:
May 18, 2023
Publication date:
July 11, 2024
Applicant:
Ecopia Tech Corporation
Inventors:
Yuanming SHU, Shuo TAN, Zihao CHEN, Ruijie DENG
Abstract: Methods and systems for generating artificial parcel data are provided. An example method involves accessing geospatial imagery depicting one or more buildings and surrounding areas, applying a machine learning model to the geospatial imagery to generate artificial parcel data in a form of a distance-transform raster map that represents a legal land parcel for each of the one or more buildings, and converting the distance-transform raster map into a vector map containing one or more polygons that represent the boundaries of each of the legal land parcels.