  Top-down Bird's Eye View (BEV) maps are a popular representation for ground
robot navigation due to their richness and flexibility for downstream tasks.
While recent methods have shown promise for predicting BEV maps from
First-Person View (FPV) images, their generalizability is limited to small
regions captured by current autonomous vehicle-based datasets. In this context,
we show that a more scalable approach towards generalizable map prediction can
be enabled by using two large-scale crowd-sourced mapping platforms, Mapillary
for FPV images and OpenStreetMap for BEV semantic maps. We introduce Map It
Anywhere (MIA), a data engine that enables seamless curation and modeling of
labeled map prediction data from existing open-source map platforms. Using our
MIA data engine, we display the ease of automatically collecting a dataset of
1.2 million pairs of FPV images & BEV maps encompassing diverse geographies,
landscapes, environmental factors, camera models & capture scenarios. We
further train a simple camera model-agnostic model on this data for BEV map
prediction. Extensive evaluations using established benchmarks and our dataset
show that the data curated by MIA enables effective pretraining for
generalizable BEV map prediction, with zero-shot performance far exceeding
baselines trained on existing datasets by 35%. Our analysis highlights the
promise of using large-scale public maps for developing & testing generalizable
BEV perception, paving the way for more robust autonomous navigation.
