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

• Published in: International journal of geographical information science : IJGIS Vol. 35; no. 9; pp. 1725 - 1745
• Main Authors: Vargas Muñoz, John E., Tuia, Devis, Falcão, Alexandre X.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.09.2021; Taylor & Francis LLC
• Subjects: Annotations; convolutional neural networks; Developing countries; Digital imaging; Digital mapping; Humanitarianism; Image annotation; Image quality; Interactive annotation; LDCs; Learning algorithms; Machine learning; Mapping; OpenStreetMap; Rural areas; Rural populations; vector maps update; very high resolution mapping; volunteered geographical information

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 a...