TY - GEN
T1 - Visualization of urban prediction using augmented maps
AU - Martedi, Sandy
AU - Callier, Sébastien
AU - Saito, Hideo
AU - Sanoamuang, Pega
AU - Muminović, Milica
PY - 2012/12/1
Y1 - 2012/12/1
N2 - In this paper, we explore a visualization method using augmented maps for urban prediction. Our implementation allows users to determine the location for prediction in a paper map. As an application example, we examine an area before and after new train station is built. We use the difference between two maps for simulating the changes or predicting the impact if a new train station is built on a location in a paper map. In off-line phase, we gather knowledge data from several reference locations by comparing two aerial maps (before and after the train station is built). We then analyze the difference of green spaces between those two maps using color extraction. We observe that the green space around the new train station mostly decreases due to the area development. This information is then stored for prediction reference. In on-line phase, we use a monocular setup that consists of one camera and a monitor display. A paper map is captured using a web camera and tracked using its geometrical features. These features can be provided using the available data from Geographical Information Systems (GIS) or automatically extracted from the texture. The map is then matched with the reference map in database. When the map is matched, we can overlay the simulation on how the green space will change due to the existence of new train stations on a new location inputted by the user.
AB - In this paper, we explore a visualization method using augmented maps for urban prediction. Our implementation allows users to determine the location for prediction in a paper map. As an application example, we examine an area before and after new train station is built. We use the difference between two maps for simulating the changes or predicting the impact if a new train station is built on a location in a paper map. In off-line phase, we gather knowledge data from several reference locations by comparing two aerial maps (before and after the train station is built). We then analyze the difference of green spaces between those two maps using color extraction. We observe that the green space around the new train station mostly decreases due to the area development. This information is then stored for prediction reference. In on-line phase, we use a monocular setup that consists of one camera and a monitor display. A paper map is captured using a web camera and tracked using its geometrical features. These features can be provided using the available data from Geographical Information Systems (GIS) or automatically extracted from the texture. The map is then matched with the reference map in database. When the map is matched, we can overlay the simulation on how the green space will change due to the existence of new train stations on a new location inputted by the user.
UR - http://www.scopus.com/inward/record.url?scp=84883858212&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883858212&partnerID=8YFLogxK
U2 - 10.1115/ESDA2012-82417
DO - 10.1115/ESDA2012-82417
M3 - Conference contribution
AN - SCOPUS:84883858212
SN - 9780791844847
T3 - ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis, ESDA 2012
SP - 335
EP - 340
BT - ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis, ESDA 2012
T2 - ASME 2012 11th Biennial Conference on Engineering Systems Design and Analysis, ESDA 2012
Y2 - 2 July 2012 through 4 July 2012
ER -