|Extracting dimensions and locations of doors, windows, and door thresholds out of mobile LiDAR data using object detection to estimate the impact of floods|Van Ackere, S.; Verbeurgt, J.; De Sloover, L.; De Wulf, A.; Van de Weghe, N.; De Maeyer, P. (2019). Extracting dimensions and locations of doors, windows, and door thresholds out of mobile LiDAR data using object detection to estimate the impact of floods. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII(3/W8): 429-436. https://hdl.handle.net/10.5194/isprs-archives-xlii-3-w8-429-2019
In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. ISPRS: Paris.
FLIAT, flood impact assessment, location of doors and windows, threshold height, object detection, machine learning
|Auteurs|| || Top |
- Van Ackere, S.
- Verbeurgt, J.
- De Sloover, L.
- De Wulf, A.
- Van de Weghe, N.
- De Maeyer, P.
Increasing urbanisation, changes in land use (e.g., more impervious area) and climate change have all led to an increasing frequency and severity of flood events and increased socio-economic impact. In order to deploy an urban flood disaster and risk management system, it is necessary to know what the consequences of a specific urban flood event are to adapt to a potential event and prepare for its impact. Therefore, an accurate socio-economic impact assessment must be conducted. Unfortunately, until now, there has been a lack of data regarding the design and construction of flood-prone building structures (e.g., locations and dimensions of doors and door thresholds and presence and dimensions of basement ventilation holes) to consider when calculating the flood impact on buildings. We propose a pipeline to detect the dimension and location of doors and windows based on mobile LiDAR data and 360° images. This paper reports on the current state of research in the domain of object detection and instance segmentation of images to detect doors and windows in mobile LiDAR data. The use and improvement of this algorithm can greatly enhance the accuracy of socio-economic impact of urban flood events and, therefore, can be of great importance for flood disaster management.