Detecting unregistered buildings from aerial images is an important task for urban management such as inspection of illegal buildings in green belt or update of GIS database. Moreover, the data acquisition platform of photogrammetry is evolving from manned aircraft to UAVs (Unmanned Aerial Vehicles). However, it is very costly and time-consuming to detect unregistered buildings from UAV images since the interpretation of aerial images still relies on manual efforts. To overcome this problem, we propose a system which automatically detects unregistered buildings from UAV images based on deep learning methods. Specifically, we train a deconvolutional network with publicly opened geospatial data, semantically segment a given UAV image into a building probability map and compare the building map with existing GIS data. Through this procedure, we could detect unregistered buildings from UAV images automatically and efficiently. We expect that the proposed system can be applied for various urban management tasks such as monitoring illegal buildings or illegal land-use change.
Abstract. Facilities such as road, parking lots play an important role in our lives nowadays. Damage to such a vehicle facility can cause human injury, as well as inconvenience and cost. To prevent this, facility monitoring is performed periodically, but the current monitoring method is low efficiency by blocking the facility or performing it late at night. In order to increase the efficiency of monitoring, research using images, especially drone images, was conducted. However, when using a drone image, there is a trade-off relationship between accuracy and processing time. In this study, we propose a real-time drone mapping based on reference images for efficient vehicle facility monitoring. The real-time drone mapping based on the reference image is composed of reference images build, aerial triangulation (AT) based on reference images (refAT), and orthophoto generation. The refAT refers to a method of performing AT by using a reference images as reference data. We compared the processing time and processing accuracy of direct georeferencing and refAT. We built 154 drone reference images in the target area. The refAT showed a processing time of about 8.95 seconds and an accuracy of 3.4 cm, and the direct georeferencing method showed a processing time of about 1.49 seconds and an accuracy of 22.5 m. If the method of this study is used for facility monitoring, it is expected that the efficiency of monitoring will be improved with speed and accuracy.
Abstract. The city of Seoul has selected Sewoon market building and its surrounding district as part of the urban regeneration zone, and currently has been promoting the project. To monitor results of the project regularly, the city has been trying to utilize a 3 dimension model of the area. In the case of buildings placed in narrow alleyways in the district, however, it is limited to generate 3D model of the buildings due to some factors. Therefore, in this study, a 3D model of façade of the building was created, using a RTK drone and action camera only. First method is to estimate of location of conjugate points using Structure from Motion, after setting conjugate points between images of the drone. Second method is to georeference action camera images by setting drone images as the reference images itself without the process of estimating location of the conjugate points. As a result of preliminary experiments to verify the two methods, the error of each method did not exceed a maximum of 0.030 m. Based on the result, we created 3D models of façade of the building in the alleyway, which is located at the intersection of Donhwamoon-ro 2 gil and Jong-ro 24 gil, and calculated absolute distance between the models. And the comparison showed that the difference was about 0.010 m on average.
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