  Cross-View Geo-Localization (CVGL) estimates the location of a ground image
by matching it to a geo-tagged aerial image in a database. Recent works achieve
outstanding progress on CVGL benchmarks. However, existing methods still suffer
from poor performance in cross-area evaluation, in which the training and
testing data are captured from completely distinct areas. We attribute this
deficiency to the lack of ability to extract the geometric layout of visual
features and models' overfitting to low-level details. Our preliminary work
introduced a Geometric Layout Extractor (GLE) to capture the geometric layout
from input features. However, the previous GLE does not fully exploit
information in the input feature. In this work, we propose GeoDTR+ with an
enhanced GLE module that better models the correlations among visual features.
To fully explore the LS techniques from our preliminary work, we further
propose Contrastive Hard Samples Generation (CHSG) to facilitate model
training. Extensive experiments show that GeoDTR+ achieves state-of-the-art
(SOTA) results in cross-area evaluation on CVUSA, CVACT, and VIGOR by a large
margin ($16.44\%$, $22.71\%$, and $17.02\%$ without polar transformation) while
keeping the same-area performance comparable to existing SOTA. Moreover, we
provide detailed analyses of GeoDTR+.
