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I am currently a Lecturer / Postdoctoral Researcher in Geoinformatics and Cartography at
Previously, I have worked and finished my PhD with highest distinction at
My research interests include:
Selected publications:
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Severe flood hits Henan, China in the late July. Typhoon In-fa landed in Zhejiang, China. This online map provides a dynamic overview of the flood-relevant Weibo posts in time and space.
With increasing urbanization, in recent years there has been a growing interest and need in monitoring and analyzing urban flood events. Social media, as a new data source, can provide real-time information for flood monitoring. The social media posts with locations are often referred to as Volunteered Geographic Information (VGI), which can reveal the spatial pattern of such events. Since more images are shared on social media than ever before, recent research focused on the extraction of flood-related posts by analyzing images in addition to texts. Apart from merely classifying posts as flood relevant or not, more detailed information, e.g. the flood severity, can also be extracted based on image interpretation. However, it has been less tackled and has not yet been applied for flood severity mapping. In this paper, we propose a novel three-step process to extract and map flood severity information. This process was applied to an image dataset collected during Hurricane Harvey in 2017, as a proof of concept.
Feng, Y., Brenner, C., & Sester, M. (2020). Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: A case study of Hurricane Harvey. ISPRS Journal of Photogrammetry and Remote Sensing, 169, 301-319.
Scale is an important aspect in cartography. From existing maps of different scales, neural network has been used to learn such scale information implicitly. The network can perform generalization directly on the mask output and generate multi-scale building maps at once. In this work, a pipeline method is proposed, which can generate multi-scale building maps from aerial imagery directly. We used a land cover classification model to provide the building blobs. With the models pre-trained for cartographic building generalization, blobs were generalized to three target map scales, 1:10,000, 1:15,000, and 1:25,000. After post-processing with vectorization and regularization, multi-scale building maps were generated and then compared with existing authoritative building data qualitatively and quantitatively. In addition, change detection was performed and suggestions for unmapped buildings could be provided at a desired map scale.
Feng, Y., Yang, C., & Sester, M. (2020). Multi-Scale Building Maps from Aerial Imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 41-47.
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Digital Terrain Models (DTMs) are essential surveying products for terrain based analyses, especially for overland flow modelling. Nowadays, many high resolution DTM products are generated by Airborne Laser Scanning (ALS). However, DTMs with even higher resolution are of great interest for a more precise overland flow modelling in urban areas. With the help of mobile mapping techniques, we can obtain much denser measurements of the ground in the vicinity of roads. A study area in Hannover, Germany was measured by a mobile mapping system. Point clouds from 485 scan strips were aligned and a DTM was extracted. In order to achieve a product with completeness, this mobile mapping produced DTM was then merged and adapted with a DTM product with 0.5 m resolution from a mapping agency. Systematic evaluations have been conducted with respect to the height accuracy of the DTM products. The results show that the final DTM product achieved a higher resolution (0.1 m) near the roads while essentially maintaining its height accuracy.
Feng, Y., Brenner, C., & Sester, M. (2018). Enhancing the resolution of urban digital terrain models using mobile mapping systems. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4.
In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens’ safety. Therefore, real-time information about such events is desirable. High quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin.
Feng, Y., & Sester, M. (2018). Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos. ISPRS International Journal of Geo-Information, 7(2), 39.