  Responding to natural disasters, such as earthquakes, floods, and wildfires,
is a laborious task performed by on-the-ground emergency responders and
analysts. Social media has emerged as a low-latency data source to quickly
understand disaster situations. While most studies on social media are limited
to text, images offer more information for understanding disaster and incident
scenes. However, no large-scale image datasets for incident detection exists.
In this work, we present the Incidents Dataset, which contains 446,684 images
annotated by humans that cover 43 incidents across a variety of scenes. We
employ a baseline classification model that mitigates false-positive errors and
we perform image filtering experiments on millions of social media images from
Flickr and Twitter. Through these experiments, we show how the Incidents
Dataset can be used to detect images with incidents in the wild. Code, data,
and models are available online at http://incidentsdataset.csail.mit.edu.
