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

• URL: http://arxiv.org/abs/2007.06749v1
• Date: Tue, 14 Jul 2020 00:51:29 GMT
• Title: Water level prediction from social media images with a multi-task ranking approach
• Authors: P. Chaudhary, S. D'Aronco, J.P. Leitao, K. Schindler, J.D. Wegner
• Abstract summary: We introduce a computer vision system that estimates water depth from social media images taken during flooding events. We propose a multi-task learning approach, where a model is trained using both a regression and a pairwise ranking loss. We 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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