An automated technique for basemap updating using UAV data

The increased reliance on geospatial data for decision-making in urban planning makes it imperative that the available spatial information is up-to-date and faithfully represents reality. This calls for map updating methods which support the integration of data from different sources in an automated manner. In this paper, we utilize existing basemap information to provide the initial data labels, thus reducing the lengthy process of label acquisition. However, we take into account that a portion of these labels are likely to be incorrect due to changes such as new constructions. We then cast the updating problem as a supervised classification with noisy training labels. Through an iterative approach, training samples which rank low on two criteria (label consistency and contextual consistency) are considered to be unreliable and removed from the training set. This technique is demonstrated in the specific context in which data obtained from an Unmanned Aerial Vehicle (UAV) is used to update building outlines in an informal settlement in Kigali, Rwanda. The proposed approach is able to accurately classify 95.34% of the UAV imagery even though the original labels are based on data obtained from outdated aerial imagery of a lower spatial resolution, causing 14.3% of the segments to have an incorrect training label. In this paper, we describe the proposed method, demonstrate the importance of both the contextual consistency and label consistency for filtering the training samples, discuss the robustness of the method to noise levels, and discuss the implications of this approach for other applications.