Deep learning from multiple crowds

• Verfasst von: Chen, Jiaoyan [VerfasserIn]
• Verfasst von: Zipf, Alexander [VerfasserIn]
• Titel: Deep learning from multiple crowds
• Titelzusatz: a case study of humanitarian mapping
• Verf.angabe: Jiaoyan Chen, Yan Zhou, Alexander Zipf, and Hongchao Fan
• Jahr: 2019
• Jahr des Originals: 2018
• Umfang: 10 S.
• Fussnoten: Date of Publication: 03 October 2018 ; Gesehen am 31.05.2019
• Titel Quelle: Enthalten in: Institute of Electrical and Electronics EngineersIEEE transactions on geoscience and remote sensing
• Ort Quelle: New York, NY : IEEE, 1964
• Jahr Quelle: 2019
• Band/Heft Quelle: 57(2019), 3, Seite 1713-1722
• DOI: doi:10.1109/TGRS.2018.2868748
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Active learning
• Sach-SW: active learning-based CNN training framework
• Sach-SW: Bing Map
• Sach-SW: building mapping
• Sach-SW: Buildings
• Sach-SW: convolutional neural nets
• Sach-SW: convolutional neural network
• Sach-SW: crowdsourced labels
• Sach-SW: crowdsourcing
• Sach-SW: deep learning
• Sach-SW: economic development
• Sach-SW: geographic information systems
• Sach-SW: ground objects
• Sach-SW: humanitarian aid
• Sach-SW: humanitarian mapping
• Sach-SW: humanitarian mapping tasks
• Sach-SW: Labeling
• Sach-SW: learning (artificial intelligence)
• Sach-SW: level-18 satellite images
• Sach-SW: Machine learning
• Sach-SW: MapSwipe
• Sach-SW: MC-CNN
• Sach-SW: mobile computing
• Sach-SW: mobile software
• Sach-SW: multiple crowds
• Sach-SW: multiple freely accessible crowdsourced geographic data
• Sach-SW: multiple metrics
• Sach-SW: numerous labels
• Sach-SW: OpenStreetMap
• Sach-SW: OsmAnd
• Sach-SW: road mapping
• Sach-SW: Roads
• Sach-SW: satellite image
• Sach-SW: Satellites
• Sach-SW: Task analysis
• Sach-SW: Training
• Sach-SW: volunteered geographic information (VGI)
• K10plus-PPN: 1666566497

Abstract

Satellite images are widely applied in humanitarian mapping that labels buildings, roads, and so on for humanitarian aid and economic development. However, the labeling now is mostly done by volunteers. In this paper, we utilize deep learning to solve humanitarian mapping tasks of a mobile software named MapSwipe. The current deep learning techniques, e.g., convolutional neural network (CNN), can recognize ground objects from satellite images but rely on numerous labels for training for each specific task. We solve this problem by fusing multiple freely accessible crowdsourced geographic data and propose an active learning-based CNN training framework named MC-CNN to deal with the quality issues of the labels extracted from these data, including incompleteness (e.g., some kinds of object are not labeled) and heterogeneity (e.g., different spatial granularities). The method is evaluated with building mapping in South Malawi and road mapping in Guinea with level-18 satellite images provided by Bing Map and volunteered geographic information from OpenStreetMap, MapSwipe, and OsmAnd. The results based on multiple metrics, including Precision, Recall, F1 Score, and area under the receiver operating characteristic curve, show that MC-CNN can fuse the crowdsourced labels for higher prediction performance and be successfully applied in MapSwipe for humanitarian mapping with 85% labor saved and an overall accuracy of 0.86 achieved.