  Cross-view geo-localization is to spot images of the same geographic target
from different platforms, e.g., drone-view cameras and satellites. It is
challenging in the large visual appearance changes caused by extreme viewpoint
variations. Existing methods usually concentrate on mining the fine-grained
feature of the geographic target in the image center, but underestimate the
contextual information in neighbor areas. In this work, we argue that neighbor
areas can be leveraged as auxiliary information, enriching discriminative clues
for geolocalization. Specifically, we introduce a simple and effective deep
neural network, called Local Pattern Network (LPN), to take advantage of
contextual information in an end-to-end manner. Without using extra part
estimators, LPN adopts a square-ring feature partition strategy, which provides
the attention according to the distance to the image center. It eases the part
matching and enables the part-wise representation learning. Owing to the
square-ring partition design, the proposed LPN has good scalability to rotation
variations and achieves competitive results on three prevailing benchmarks,
i.e., University-1652, CVUSA and CVACT. Besides, we also show the proposed LPN
can be easily embedded into other frameworks to further boost performance.
