Information on the depth of floodwater is crucial for rapid mapping of areas
affected by floods. However, previous approaches for estimating floodwater
depth, including field surveys, remote sensing, and machine learning
techniques, can be time-consuming and resource-intensive. This paper presents
an automated and fast approach for estimating floodwater depth from on-site
flood photos. A pre-trained large multimodal model, GPT-4 Vision, was used
specifically for estimating floodwater. The input data were flooding photos
that contained referenced objects, such as street signs, cars, people, and
buildings. Using the heights of the common objects as references, the model
returned the floodwater depth as the output. Results show that the proposed
approach can rapidly provide a consistent and reliable estimation of floodwater
depth from flood photos. Such rapid estimation is transformative in flood
inundation mapping and assessing the severity of the flood in near-real time,
which is essential for effective flood response strategies.

用于估计洪水深度的自动化快速方法，利用洪水照片和 GPT-4 Vision 模型，通过参考物体的高度返回洪水深度估计结果。该方法能够快速、准确地评估洪水深度，对洪水内涝图绘制和洪水严重程度的评估具有重要意义。

利用大规模多模型自动估计洪水深度进行快速洪水制图

Automated Floodwater Depth Estimation Using Large Multimodal Model for  Rapid Flood Mapping

Spatial information on cropland distribution, often called cropland or crop
maps, are critical inputs for a wide range of agriculture and food security
analyses and decisions. However, high-resolution cropland maps are not readily
available for most countries, especially in regions dominated by smallholder
farming (e.g., sub-Saharan Africa). These maps are especially critical in times
of crisis when decision makers need to rapidly design and enact
agriculture-related policies and mitigation strategies, including providing
humanitarian assistance, dispersing targeted aid, or boosting productivity for
farmers. A major challenge for developing crop maps is that many regions do not
have readily accessible ground truth data on croplands necessary for training
and validating predictive models, and field campaigns are not feasible for
collecting labels for rapid response. We present a method for rapid mapping of
croplands in regions where little to no ground data is available. We present
results for this method in Togo, where we delivered a high-resolution (10 m)
cropland map in under 10 days to facilitate rapid response to the COVID-19
pandemic by the Togolese government. This demonstrated a successful transition
of machine learning applications research to operational rapid response in a
real humanitarian crisis. All maps, data, and code are publicly available to
enable future research and operational systems in data-sparse regions.

针对在没有足够地面数据的情况下快速制定农地地图的需求，该研究提出了一种利用机器学习进行快速地图绘制的方法，并在 10 天内为多哥制定了高分辨率农地地图，以应对新冠肺炎疫情带来的问题。