Deep Learning with Satellite Images and Volunteered Geographic Information

2017 ◽  
pp. 63-78
Author(s):  
Jiaoyan Chen ◽  
Alexander Zipf
Keyword(s):  
Deep Learning ◽  
Satellite Images ◽  
Volunteered Geographic Information ◽  
Geographic Information

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

scholarly journals Segmentation based traversing-agent approach for road width extraction from satellite images using volunteered geographic information

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Prajowal Manandhar ◽  
Prashanth Reddy Marpu ◽  
Zeyar Aung
Keyword(s):  
Image Segmentation ◽  
Satellite Images ◽  
Autonomous Agents ◽  
Image Data ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Full Extent ◽  
The Road ◽  
Road Width ◽  
Road Segments

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

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
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