Object Delineation in Satellite Images
This addresses a specific need in geospatial applications for efficient object extraction from satellite data, but it is incremental as it builds on existing ML detection methods.
The paper tackles the problem of converting ML-detected objects in satellite images from raster to geospatial vector form, delivering a simple and lightweight algorithm for exact delineation that allows for further simplification as needed.
Machine learning is being widely applied to analyze satellite data with problems such as classification and feature detection. Unlike traditional image processing algorithms, geospatial applications need to convert the detected objects from a raster form to a geospatial vector form to further analyze it. This gem delivers a simple and light-weight algorithm for delineating the pixels that are marked by ML algorithms to extract geospatial objects from satellite images. The proposed algorithm is exact and users can further apply simplification and approximation based on the application needs.