Automated Image-to-Map Discrepancy Detection using Iterative Trimming

Abstract

Keeping existing vector databases Lip to date is a real challenge for GIS data providers. This study directly compares a a map with a more recent image in order to detect the discrepancies between them. An automatic workflow was designed to process the image based on existing information extracted from the vector database. First, geographic object-based image anal is provided automatically labeled image segments after matching, the vector database to the image. Then, discrepancies were detected using a statistical iterative trimming, where outliers were excluded based on a likelihood threshold. Applied on forest map updating, the proposed workflow, was able to detect about 75 percent of the forest regeneration, and 100 percent of the clear cuts with less than 10 percent of commission errors. This discrepancy detection approach assumes that discrepancy corresponds to small proportion of the map area and is very promising in diverse applications thanks to its flexibility.