A D8compatible highefficient channel head recognition method Abstract   As the resolution of DEMs is becoming higher, both the efficiency and accuracy of channel head recognition are important for drainage network extraction. In this paper, a D8compatible highefficient channel head recognition method is proposed. For each potential channel, this method first calculates a geomorphologic parameter series along the flow path, and then determines the channel head by detecting the change point in the series. Instead of directly using one threshold value for a whole region that is still commonly used in D8based methods, the proposed method recognizes channel heads by their local geomorphology one by one. The proposed method is applied with high resolution DEMs for different terrains, and the identified channel heads and extracted drainage networks show good agreement with observations. In comparison with a popular software with stateoftheart channel head recognition method, the proposed one shows similar accuracy but much higher computational efficiency.

Time budget evaluation for imagebased reconstruction of sewer shafts In this paper we propose a robust realtime image and sensor based approach for automatic 3d model acquisition of sewer shafts from survey videos captured by a downwardlooking fisheyelens camera while lowering it into the shaft. Our approach is based on Structure from Motion adjusted to the constrained motion and scene, and involves shape recognition techniques in order to obtain the geometry of the scene appropriately. We perform a time budget evaluation for the components of an existing offline application based on previous work and design a realtime application which can be applied during onsite inspection. The methods of our approach are modified so that they can be executed on the GPU. Expensive bundle adjustment is avoided by applying a simple and fast geometric correction of the computed reconstruction which is capable of handling inaccuracies of the intrinsic camera calibration parameters.