  Natural disasters, such as floods, tornadoes, or wildfires, are increasingly
pervasive as the Earth undergoes global warming. It is difficult to predict
when and where an incident will occur, so timely emergency response is critical
to saving the lives of those endangered by destructive events. Fortunately,
technology can play a role in these situations. Social media posts can be used
as a low-latency data source to understand the progression and aftermath of a
disaster, yet parsing this data is tedious without automated methods. Prior
work has mostly focused on text-based filtering, yet image and video-based
filtering remains largely unexplored. In this work, we present the Incidents1M
Dataset, a large-scale multi-label dataset which contains 977,088 images, with
43 incident and 49 place categories. We provide details of the dataset
construction, statistics and potential biases; introduce and train a model for
incident detection; and perform image-filtering experiments on millions of
images on Flickr and Twitter. We also present some applications on incident
analysis to encourage and enable future work in computer vision for
humanitarian aid. Code, data, and models are available at
http://incidentsdataset.csail.mit.edu.
