  Choropleth maps are a common visual representation for region-specific
tabular data and are used in a number of different venues (newspapers,
articles, etc). These maps are human-readable but are often challenging to deal
with when trying to extract data for screen readers, analyses, or other related
tasks. Recent research into Visual-Question Answering (VQA) has studied
question answering on human-generated charts (ChartQA), such as bar, line, and
pie charts. However, little work has paid attention to understanding maps;
general VQA models, and ChartQA models, suffer when asked to perform this task.
To facilitate and encourage research in this area, we present MapQA, a
large-scale dataset of ~800K question-answer pairs over ~60K map images. Our
task tests various levels of map understanding, from surface questions about
map styles to complex questions that require reasoning on the underlying data.
We present the unique challenges of MapQA that frustrate most strong baseline
algorithms designed for ChartQA and general VQA tasks. We also present a novel
algorithm, Visual Multi-Output Data Extraction based QA (V-MODEQA) for MapQA.
V-MODEQA extracts the underlying structured data from a map image with a
multi-output model and then performs reasoning on the extracted data. Our
experimental results show that V-MODEQA has better overall performance and
robustness on MapQA than the state-of-the-art ChartQA and VQA algorithms by
capturing the unique properties in map question answering.
