Assessing the Value of Commercial Data with FloodPlanet, a High-Resolution Commercial Imagery Flood Dataset, for Inundation Detection

Keywords: Flood Detection, Commercial Satellite, Deep Learning
Abstract Type: Paper Abstract

Abstract

Floods cause more damage than any other disaster. Mapping floods from public and commercial, optical, and radar satellites improve response, relief, and mitigation efforts to save money, lives, and property. The advancement in deep learning and increased spatial resolution of commercial satellites like Planetscope provide an opportunity to improve flood detection. However, as is often the case with data-driven science, the lack of training and validation data and its inadequacy is a major obstacle to deep learning in flood detection.

The newly proposed FloodPlanet dataset reduces this barrier. FloodPlanet is a multi-sensor, co-located, labeled, spatial-temporal, commercial SmallSat based dataset to test, train, and validate deep learning algorithms for surface water detection. The dataset contains high-resolution labels of surface water based on PlanetScope commercial data for 18 flood events across the globe, along with associated Sentinel-1, Sentinel-2, or Landsat 8 imagery.

. Experiments were designed to assess the value of the commercial SmallSat dataset to train deep learning algorithms for surface water detection based on this dataset. We first compare the performance difference of the proposed model trained on labeled public data with their associated public label and FloodPlanet. We then train the proposed model using FloodPlanet’s high-resolution label on public data to see the performance difference from using FloodPlanet only. We took advantage of the increased spatial and temporal resolution of commercial satellites to produce high-resolution labels, aiming to improve surface water detection using publicly available data during flood events through advanced deep learning approaches.