Name
Abstract | ||
|---|---|---|
The advancements of sensing technologies, including remote sensing, in situ sensing, social sensing, and health sensing, have tremendously improved our capability to observe and record natural and social phenomena, such as natural disasters, presidential elections, and infectious diseases. The observations have provided an unprecedented opportunity to better understand and respond to the spatiotemporal dynamics of the environment, urban settings, health and disease propagation, business decisions, and crisis and crime. Spatiotemporal event detection serves as a gateway to enable a better understanding by detecting events that represent the abnormal status of relevant phenomena. This paper reviews the literature for different sensing capabilities, spatiotemporal event extraction methods, and categories of applications for the detected events. The novelty of this review is to revisit the definition and requirements of event detection and to layout the overall workflow (from sensing and event extraction methods to the operations and decision-supporting processes based on the extracted events) as an agenda for future event detection research. Guidance is presented on the current challenges to this research agenda, and future directions are discussed for conducting spatiotemporal event detection in the era of big data, advanced sensing, and artificial intelligence. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1080/17538947.2020.1738569 | INTERNATIONAL JOURNAL OF DIGITAL EARTH |
Keywords | DocType | Volume |
GeoAI, geography and geoscience, human dynamics, digital earth, computational challenges, cloud computing, internet of things | Journal | 13 |
Issue | ISSN | Citations |
12 | 1753-8947 | 2 |
PageRank | References | Authors |
0.40 | 0 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manzhu Yu | 1 | 49 | 6.41 |
| Myra Bambacus | 2 | 140 | 9.05 |
| Guido Cervone | 3 | 44 | 11.29 |
| Keith C. Clarke | 4 | 412 | 60.84 |
| Daniel Q. Duffy | 5 | 62 | 5.54 |
| Qunying Huang | 6 | 420 | 29.69 |
| Jing Li | 7 | 17 | 4.27 |
| Wenwen Li | 8 | 321 | 26.87 |
| Zhenlong Li | 9 | 129 | 11.81 |
| Qian Liu | 10 | 3 | 2.12 |
| Bernd Resch | 11 | 2 | 0.40 |
| Jingchao Yang | 12 | 7 | 1.52 |
| Chaowei Yang | 13 | 841 | 56.47 |