Curating flood extent data and leveraging citizen science for benchmarking machine learning solutions

Gahlot, Shubhankar; Ramasubramanian, Muthukumaran; Gurung, Iksha; Hänsch, Ronny; Molthan, Andrew; Maskey, Manil

doi:10.1002/essoar.10511103.1

Cited by 1 publication

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“…Several public benchmark datasets exist addressing flood detection and related topics. The Flood Extent Detection dataset [7] published in 2021 by NASA Impact provides more than 30k 256 × 256 dual-polarimetric Sentinel 1 image patches. The goal is to detect open areas of flood water.…”

Section: Introductionmentioning

confidence: 99%

SpaceNet 8 - The Detection of Flooded Roads and Buildings

Hänsch

Arndt

Lunga

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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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 850km 2 , including 32k buildings and 1,300km roads of which 13% and 15% are flooded, respectively.

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Section: Introductionmentioning

confidence: 99%