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

arXiv: https://arxiv.org/abs/2007.06749 · HTML: https://mlsanity.com/paper/2007.06749/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Towards a Collective Agenda on AI for Earth Science Data Analysis](https://mlsanity.com/paper/2104.05107/) (2021)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications](https://mlsanity.com/paper/2305.09018/) (2023)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping](https://mlsanity.com/paper/2402.16684/) (2024)
- [Deep Sensing of Urban Waterlogging](https://mlsanity.com/paper/2103.05927/) (2021)
- [MultiFloodSynth: Multi-Annotated Flood Synthetic Dataset Generation](https://mlsanity.com/paper/2502.03966/) (2025)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)

## Abstract (arXiv)

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.