It’s easy to distinguish a lake from a flood. But when you’re looking at an aerial photograph, factors like angle, altitude, cloud cover, and context can make the task more difficult. And when you need to identify 100,000 aerial images in order to give first responders the information they need to accelerate disaster response efforts? That’s when you need to combine the speed and accuracy of machine learning (ML) with the precision of human judgement.
With a constant supply of low altitude disaster imagery and satellite imagery coming online, researchers are looking for faster and more affordable ways to label this content so that it can be utilized by stakeholders like first responders and state, local, and federal agencies. Because the process of labeling this data is expensive, manual, and time consuming, developing ML models that can automate image labeling (or annotation) is critical to bringing this data into a more usable state. And to develop an effective ML model, you need a ground truth dataset: a labeled set of data that is used to train your model. The lack of an adequate ground truth dataset for LADI images put model development out of reach until now.
A broad array of organizations and agencies are developing solutions to this problem, and Amazon is there to support them with technology, infrastructure, and expertise. By integrating the full suite of human-in-the-loop services into a single AWS data pipeline, we can improve model performance, reduce the cost of human review, simplify the process of implementing an annotation pipeline, and provide prebuilt templates for the worker user interface, all while supplying access to an elastic, on-demand Amazon Mechanical Turk workforce that can scale to natural disaster event-driven annotation task volumes.
One of the projects that has made headway in the annotation of disaster imagery was developed by students at Penn State. Working alongside a team of MIT Lincoln Laboratory researchers, students at Penn State College of Information Sciences and Technology (IST) developed a computer model that can improve the classification of disaster scene images and inform disaster response.
The Penn State project began with an analysis of imagery from the Low Altitude Disaster Imagery (LADI) dataset, a collection of aerial images taken above disaster scenes since 2015. Based on work supported by the United States Air Force, the LADI dataset was developed by the New Jersey Office of Homeland Security and Preparedness and MIT Lincoln Laboratory, with support from the National Institute of Standards and Technology’s Public Safety Innovation Accelerator Program (NIST PSIAP) and AWS.
“We met with the MIT Lincoln Laboratory team in June 2019 and recognized shared goals around impro
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