... sensor on the smartphone has high accuracy and no additional cost, and thus can be applied to traffic detection ... crowd-sensing data can improve the sensing precision and enhance the credibility of the data through cleaning and ...
C.H. Chen. 14.1.1 LABEL COLLECTION APPROACHES IN REMOTE SENSING Addressing labelled data scarcity in remote sensing ... errors in the extracted labels. In addi- tion, the map can be obsolete, thus land-cover changes are not ...
... crowdsourcing data , new data models for big data , and human dynamics . Machine learning methods are derived from the development of artificial intelligence ( AI ) and statistic models . The GIScience community has developed a few ...
... data. However, because of the problems of human labeling error, labeling cost, and time consumption, it is required to increase the performance of deep learning for unsupervised activity recognition. The knowledge extracted from crowd- ...
The MDM series of conferences, since its debut in 1999, has established itself as a prestigious forum for the exchange of innovative and significant research results in mobile data management The conference provides unique opportunities to ...
... smart phones whereas we used research-grade accelerometers and GPS devices mounted at specific locations. Although smartphones are becom- ing ubiquitous technologies for continuous sensing of geolocation and acceleration data, they are ...
... Sensor-based activity recognition, in IEEE Transactions on Systems, Man and Cybernetics 2012 H. Cooper, E.-J. Ong, N ... data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011) N. Gayar, F. Schwenker, G. Palm, A study of the ...
... detection for health applications are as follows: 1) Deep learning models can automatically extract the optimal features directly from raw spatiotemporal gait data without the need for data preprocessing or engineering. (Costilla- Reyes ...