INDOOR-3.6M : A Multi-Modal Image Dataset for Indoor Geolocation
Keywords: geolocation, multimodal, indoor, deep learning, dataset benchmark, geolocalization
TL;DR: We intoduce an indoor image geolocation dataset with an accompanying benchmark test set.
Abstract: Indoor image geolocation, the task of determining the location of an indoor scene
based on visual content, presents unique challenges due to the constrained and
repetitive nature of indoor spaces. Current geolocation methods, while advanced
in outdoor contexts, struggle to perform accurately in indoor environments due to
the lack of diverse and representative indoor datasets. To address this gap, we in-
troduce INDOOR-3.6M, a large-scale dataset of geotagged indoor imagery span-
ning various residential, commercial, and public spaces from around the world.
In addition to the dataset, we propose a new sampling methodology to ensure ge-
ographic diversity and balance. We also introduce INDOOR-15K, a benchmark
for evaluating indoor-specific geolocation models. Finally, we demonstrate the
dataset’s utility by finetuning GeoCLIP using our dataset, which shows significant
improvements over the GeoCLIP baseline on our test set and other benchmark test
sets.
Supplementary Material: zip
Primary Area: datasets and benchmarks
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Submission Number: 11640
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