Human Assisted Artificial Intelligence Based Technique to Create Natural Features for OpenStreetMap

TL;DR

This paper introduces an AI-assisted method leveraging satellite imagery and human input to efficiently generate natural features in OpenStreetMap, enhancing map accuracy and detail through interactive machine learning.

Contribution

It presents a novel interactive machine learning approach combining human validation with satellite image analysis to improve feature creation in OSM.

Findings

Effective extraction of natural features from satellite images.

Enhanced collaboration between AI and human editors.

Improved accuracy and efficiency in map feature creation.

Abstract

In this work, we propose an AI-based technique using freely available satellite images like Landsat and Sentinel to create natural features over OSM in congruence with human editors acting as initiators and validators. The method is based on Interactive Machine Learning technique where human inputs are coupled with the machine to solve complex problems efficiently as compare to pure autonomous process. We use a bottom-up approach where a machine learning (ML) pipeline in loop with editors is used to extract classes using spectral signatures of images and later convert them to editable features to create natural features.