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Nowadays, advanced remote sensing technologies provide huge amount of Earth Observation (EO) data timely. Growing quickly in terms of size and structure, EO data require a new way of handling and processing as it is considered big data. Cloud-computing platform proved to be a reliable and scalable platform that suits various user demands in remote sensing data processing. To verify the ambiguity of information derived solely from remote sensing, ground data is vital. The only way to keep pace with big remote sensing data is to exploit the crowdsourced data, which has been recently proposed elsewhere. In this study, we developed a prototype of an integrated location based service on top of cloud computing platform to detect land cover features and engage the crowd of volunteers during training and verification process. Relying on open-source tools, the proposed system provides location-based data collection and satellite image classification. The prototype was tested over the rapid on-going landscape surrounding the University of Nottingham, Malaysia campus. More advanced functions will be developed and a full system will be deployed and tested in further study.
Ahmed, Moataz; Huynh, Dai; Wickramasinghe, Darshana; and Vu, Tuong-Thuy
"CROWD-2-CLOUD – Remote Sensing Land Cover Verification With Crowd-Sourcing Data,"
Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings: Vol. 15
, Article 5.
Available at: https://scholarworks.umass.edu/foss4g/vol15/iss1/5