Street-Level Landmarks Acquisition Based on SVM Classifiers

High-density street-level reliable landmarks are one of the important foundations for street-level geolocation. However, the existing methods cannot obtain enough street-level landmarks in a short period of time. In this paper, a street-level landmarks acquisition method based on SVM (Support Vector Machine) classifiers is proposed. Firstly, the port detection results of IPs with known services are vectorized, and the vectorization results are used as an input of the SVM training. Then, the kernel function and penalty factor are adjusted for SVM classifiers training, and the optimal SVM classifiers are obtained. After that, the classifier sequence is constructed, and the IPs with unknown service are classified using the sequence. Finally, according to the domain name corresponding to the IP, the relationship between the classified server IP and organization name is established. The experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially, and the median geolocation error using evaluated landmarks is reduced by about 2 km.