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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Vargas Munoz, John E. [1] ; Tuia, Devis [2] ; Falcao, Alexandre X. [1]
Total Authors: 3
Affiliation:
[1] Univ Estadual Campinas, Lab Image Data Sci, Inst Comp, Campinas - Brazil
[2] Wageningen Univ & Res, Lab Geoinformat Sci & Remote Sensing, Wageningen - Netherlands
Total Affiliations: 2
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE; v. 35, n. 9, p. 1725-1745, SEP 2 2021.
Web of Science Citations: 3
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. (AU)

FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 16/14760-5 - Interactive Annotation of Remote Sensing Images
Grantee:John Edgar Vargas Muñoz
Support Opportunities: Scholarships in Brazil - Doctorate