scholarly journals Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Jiangye Yuan ◽  
Pranab K. Roy Chowdhury ◽  
Jacob McKee ◽  
Hsiuhan Lexie Yang ◽  
Jeanette Weaver ◽  
...  
Keyword(s):  
Deep Learning ◽  
Volunteered Geographic Information ◽  
Geographic Information

Volunteered geographic information

Geography ◽  
2014 ◽  
Vol 99 (3) ◽  
pp. 157-160
Author(s):  
Doreen S. Boyd ◽  
Giles M. Foody
Keyword(s):  
Volunteered Geographic Information ◽  
Geographic Information

Assessing The Quality of Water Depth Derived From Volunteered Geographic Information (VGI) For Flood Monitoring. A Case Study of Hurricane Harvey In Harris County, Texas

2021 ◽  
Author(s):  
Abdullatif Alyaqout ◽  
T. Edwin Chow ◽  
Alexander Savelyev
Keyword(s):  
Water Depth ◽  
Temporal Distribution ◽  
Volunteered Geographic Information ◽  
Temporal Variations ◽  
Geographic Information ◽  
Text Messages ◽  
Quality Of Water ◽  
Significant Difference ◽  
Flood Monitoring

Abstract The primary objectives of this study are to 1) assess the quality of each volunteered geographic information (VGI) data modality (text, pictures, and videos), and 2) evaluate the quality of multiple VGI data sources, especially the multimedia that include pictures and videos, against synthesized water depth (WD) derived from remote sensing (RS) and authoritative data (e.g. stream gauges and depth grids). The availability of VGI, such as social media and crowdsourced data, empowered the researchers to monitor and model floods in near-real-time by integrating multi-sourced data available. Nevertheless, the quality of VGI sources and its reliability for flood monitoring (e.g. WD) is not well understood and validated by empirical data. Moreover, existing literature focuses mostly on text messages but not the multimedia nature of VGI. Therefore, this study measures the differences in synthesized WD from VGI modalities in terms of (1) spatial and (2) temporal variations, (3) against WD derived from RS, and (4) against authoritative data including (a) stream gauges and (b) depth grids. The results of the study show that there are significant differences in terms of spatial and temporal distribution of VGI modalities. Regarding VGI and RS comparison, the results show that there is a significant difference in WD between VGI and RS. In terms of VGI and authoritative data comparison, the analysis revealed that there is no significant difference in WD between VGI and stream gauges, while there is a significant difference between the depth grids and VGI.

scholarly journals The Good, the Bad, and the Uncertain: Contributions of Volunteered Geographic Information to Community Disaster Resilience

2018 ◽  
Vol 6 ◽  
Author(s):  
Billy Tusker Haworth ◽  
Eleanor Bruce ◽  
Joshua Whittaker ◽  
Róisín Read
Keyword(s):  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Disaster Resilience ◽  
Community Disaster Resilience

scholarly journals Structures data collection for the national map using volunteered geographic information

2012 ◽  
Author(s):  
Barbara S. Poore ◽  
Eric B. Wolf ◽  
Erin M. Korris ◽  
Jennifer L. Walter ◽  
Greg D. Matthews
Keyword(s):  
Data Collection ◽  
Volunteered Geographic Information ◽  
Geographic Information
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