The number of applications associated with OpenStreetMap (OSM), one of the most famous crowd-sourced projects for volunteered geographic information (VGI), have increased because OSM data is both 'free' and 'up-to-date'. However, limited by the ability of the providers, the quality of the collected data remains a valid concern. This work focuses on how to assess the quality of OSM via deep learning and high-resolution remote imagery. First, considering that high-resolution remote sensing imagery is clear enough for recognizing buildings, we proposed using multi-task deep-convolutional networks to extract buildings in pixel level. The extracted buildings were converted into polygons with geographical coordinates, which were treated as reference data. Then, OSM footprint data were matched with the reference data. Quality was assessed in terms of both positional accuracy and data completeness. Finally, the building footprint data of OSM for the city of Las Vegas, Nevada, USA, were evaluated. The experiments show that the proposed method can assess OSM effectively and accurately. The results show that building extracted by the proposed method can be treated as a new data source for assessing OSM quality and can also be used for urban planning in regions where OSM lacks building data.

The inertial navigation system has high short-term positioning accuracy but features cumulative error. Although no cumulative error occurs in WiFi fingerprint localization, mismatching is common. A popular technique thus involves integrating an inertial navigation system with WiFi fingerprint matching. The particle filter uses dead reckoning as the state transfer equation and the difference between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall; if so, the weight is set to zero. For particles that do not cross the wall, considering the distance between current and historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of less trusted particles. This paper also proposes a multidimensional Euclidean distance algorithm to reduce WiFi fingerprint mismatching. Experimental results indicate that the proposed algorithm achieves high positioning accuracy.

With the increasing demand for wireless location services, it is of great interest to reduce the deployment cost of positioning systems. For this reason, indoor positioning based on WiFi has attracted great attention. Compared with the received signal strength indicator (RSSI), channel state information (CSI) captures the radio propagation environment more accurately. However, it is necessary to take signal bandwidth, interferences, noises, and other factors into account for accurate CSI-based positioning. In this paper, we propose a novel dictionary filtering method that uses the direct weight determination method of a neural network to denoise the dictionary and uses compressive sensing (CS) to extract the channel impulse response (CIR). A high-precision time-of-arrival (TOA) is then estimated by peak search. A median value filtering algorithm is used to locate target devices based on the time-difference-of-arrival (TDOA) technique. We demonstrate the superior performance of the proposed scheme experimentally, using data collected with a WiFi positioning testbed. Compared with the fingerprint location method, the proposed location method does not require a site survey in advance and therefore enables a fast system deployment.