Object Delineation in Satellite Images

TL;DR

This paper presents a simple, lightweight algorithm for converting machine learning-identified pixels in satellite images into precise geospatial vector objects, facilitating further analysis.

Contribution

It introduces an exact, easy-to-implement algorithm for delineating objects in satellite images, bridging raster detection and vector analysis.

Findings

The algorithm accurately delineates objects from satellite images.

It is lightweight and adaptable for various applications.

Users can customize the level of simplification.

Abstract

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.