Deducing trip-related information from web-scale datasets has received large amounts of attention recently. Identifying points of interest (POIs) in geo-tagged photos is one of these problems. The problem can be viewed as a standard clustering problem of partitioning two-dimensional objects. In this work, we study spectral clustering, which is the first attempt for the identification of POIs. However, there is no unified approach to assigning the subjective clustering parameters, and these parameters vary immensely in different metropolitans and locations. To address this issue, we study a self-tuning technique that can properly determine the parameters for the clustering needed. Besides geographical information, web photos inherently store other rich information. Such heterogenous information can be used to enhance the identification accuracy. Thereby, we study a novel refinement framework that is based on the tightness and cohesion degree of the additional information. We thoroughly demonstrate our findings by web-scale datasets collected from Flickr.