Name
Title | ||
|---|---|---|
Performance improvement on a Web Geospatial service for the remote sensing flood-induced crop loss assessment web application using vector tiling |
Abstract | ||
|---|---|---|
The Remote Sensing Flood Crop Loss Assessment (RF-CLASS) is a system of geospatial Web services that provide comprehensive services to decision makers in assessing flood-induced crop loss. It serves a series of vector-based geospatial data. Traditionally, these data are visualized on the Web through a open Web Map Service (WMS) while the geospatial data are kept in either a geo-database or a vector-feature persistent service - Web Feature Service (WFS). The rasterization of vector features for rendering on the Web is often completely on the fly. This approach leads to a significant inferior user experience on visualization of large vector dataset due to the delay on rasterization without pre-calculated pyramids and the loss of rich attributions on each vector feature. The vector tiling technology is adopted to improve the performance. Several technical challenges in optimizing the vector tiling have been identified and studied that are tiling schema, tile boundary, and attribution. Full stack of services (server and client) have been designed, implemented and tested to achieve the best performance on loading speed and attribution. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/Agro-Geoinformatics.2017.8047053 | 2017 6th International Conference on Agro-Geoinformatics |
Keywords | Field | DocType |
geospatial Web service,vector tiling,vector tile service,geospatial Web client,crop loss,flood,remote sensing | Web Feature Service,Web Map Service,Geospatial analysis,Data mining,Web mapping,Visualization,Computer science,Remote sensing,Web application,Web service,Database,Web Coverage Service | Conference |
ISSN | ISBN | Citations |
2334-3168 | 978-1-5386-3885-9 | 0 |
PageRank | References | Authors |
0.34 | 3 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eugene G. Yu | 1 | 2 | 2.42 |
| Liping Di | 2 | 811 | 98.92 |
| Md. Shahinoor Rahman | 3 | 7 | 3.99 |
| Li Lin | 4 | 44 | 23.07 |
| Chen Zhang | 5 | 8 | 3.31 |
| Lei Hu | 6 | 10 | 3.35 |
| ranjay shrestha | 7 | 9 | 3.84 |
| Lingjun Kang | 8 | 0 | 0.34 |
| Junmei Tang | 9 | 6 | 3.57 |
| Guangyuan Yang | 10 | 1 | 0.70 |