TY - GEN
T1 - SpaceNet 8 - The Detection of Flooded Roads and Buildings
AU - Hansch, Ronny
AU - Arndt, Jacob
AU - Lunga, Dalton
AU - Gibb, Matthew
AU - Pedelose, Tyler
AU - Boedihardjo, Arnold
AU - Petrie, Desiree
AU - Bacastow, Todd M.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The frequency and intensity of natural disasters (i.e. wildfires, storms, floods) has increased over recent decades. Extreme weather can often be linked to climate change, and human population expansion and urbanization have led to a growing risk. In particular floods due to large amounts of rainfall are of rising severity and are causing loss of life, destruction of buildings and infrastructure, erosion of arable land, and environmental hazards around the world. Expanding urbanization along rivers and creeks often includes opening flood plains for building construction and river straightening and dredging speeding up the flow of water. In a flood event, rapid response is essential which requires knowledge which buildings are susceptible to flooding and which roads are still accessible. To this aim, SpaceNet 8 is the first remote sensing machine learning training dataset combining building footprint detection, road network extraction, and flood detection covering 850km2, including 32k buildings and 1,300km roads of which 13% and 15% are flooded, respectively.
AB - The frequency and intensity of natural disasters (i.e. wildfires, storms, floods) has increased over recent decades. Extreme weather can often be linked to climate change, and human population expansion and urbanization have led to a growing risk. In particular floods due to large amounts of rainfall are of rising severity and are causing loss of life, destruction of buildings and infrastructure, erosion of arable land, and environmental hazards around the world. Expanding urbanization along rivers and creeks often includes opening flood plains for building construction and river straightening and dredging speeding up the flow of water. In a flood event, rapid response is essential which requires knowledge which buildings are susceptible to flooding and which roads are still accessible. To this aim, SpaceNet 8 is the first remote sensing machine learning training dataset combining building footprint detection, road network extraction, and flood detection covering 850km2, including 32k buildings and 1,300km roads of which 13% and 15% are flooded, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85137762617&partnerID=8YFLogxK
U2 - 10.1109/CVPRW56347.2022.00153
DO - 10.1109/CVPRW56347.2022.00153
M3 - Conference contribution
AN - SCOPUS:85137762617
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 1471
EP - 1479
BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
PB - IEEE Computer Society
T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Y2 - 19 June 2022 through 20 June 2022
ER -
