Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient

John E. Vargas Muñoz, Devis Tuia, Alexandre X. Falcão

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Locating populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas. Recent efforts have tackled this problem as the detection of buildings in aerial images. However, the quality and the amount of rural building annotated data in open mapping services like OpenStreetMap (OSM) is not sufficient for training accurate models for such detection. Although these methods have the potential of aiding in the update of rural building information, they are not accurate enough to automatically update the rural building maps. In this paper, we explore a human-computer interaction approach and propose an interactive method to support and optimize the work of volunteers in OSM. The user is asked to verify/correct the annotation of selected tiles during several iterations and therefore improving the model with the new annotated data. The experimental results, with simulated and real user annotation corrections, show that the proposed method greatly reduces the amount of data that the volunteers of OSM need to verify/correct. The proposed methodology could benefit humanitarian mapping projects, not only by making more efficient the process of annotation but also by improving the engagement of volunteers.
Original languageEnglish
Number of pages21
JournalInternational Journal of Geographical Information Science
DOIs
Publication statusE-pub ahead of print - 28 Aug 2020

Fingerprint Dive into the research topics of 'Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient'. Together they form a unique fingerprint.

Cite this