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With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies. [ABSTRACT FROM AUTHOR]

Flood-related image datasets from social media, smartphones, CCTV cameras, and unmanned aerial vehicles (UAVs) present valuable data for the management of flood risk, and particularly for the application of modern convolutional neural networks (CNNs) to specific flood-related problems such as flood extent detection and flood depth estimation. This review discusses the increasing role of CNNs in flood research with a growing number of published datasets, particularly since 2018. We note the lack of open and labelled flood image datasets and the growing need for an open, benchmark data library for image classification, object detection, and segmentation relevant to flood management. Such a library would provide benchmark datasets to advance CNN flood applications in general and serve as a resource, providing data scientists and the flood research community with the necessary data for model training and validation. [ABSTRACT FROM AUTHOR]

We propose a method for spatial downscaling of Landsat 8-derived LST maps from 100(30 m) resolution down to 2–4 m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40–1.83 K prior to and 0.76–1.21 K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales. [ABSTRACT FROM AUTHOR]

Timely clearing-up interventions are essential for effective recovery of flood-damaged housing, however, time-consuming door-to-door inspections for insurance purposes need to take place before major repairs can be done to adequately assess the losses caused by flooding. With the increased probability of flooding, there is a heightened need for rapid flood damage assessment methods. High resolution imagery captured by unmanned aerial vehicles (UAVs) offers an opportunity for accelerating the time needed for inspections, either through visual interpretation or automated image classification. In this study, object-oriented image segmentation coupled with tree-based classifiers was implemented on a 10 cm resolution RGB orthoimage, captured over the English town of Cockermouth a week after a flood triggered by storm Desmond, to automatically detect debris associated with damages predominantly to residential housing. Random forests algorithm achieved a good level of overall accuracy of 74%, with debris being correctly classified at the rate of 58%, and performing well for small debris (67%) and skips (64%). The method was successful at depicting brightly-colored debris, however, was prone to misclassifications with brightly-colored vehicles. Consequently, in the current stage, the methodology could be used to facilitate visual interpretation of UAV images. Methods to improve accuracy have been identified and discussed. [ABSTRACT FROM AUTHOR]