Water level prediction from social media images with a multi-task ranking approach

TL;DR

This paper presents a multi-task deep learning system that estimates water depth from social media images during floods, enabling near real-time flood mapping with limited annotated data.

Contribution

It introduces a novel multi-task ranking approach for water level prediction and provides a new dataset, DeepFlood, for training and evaluation.

Findings

Achieves ~11 cm RMSE in water level prediction from single images

Efficiently learns from small annotated datasets and larger weakly annotated datasets

Demonstrates potential for real-time flood mapping using social media images

Abstract

Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is diffcult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to effciently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named DeepFlood, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with ~11 cm root mean square error.