LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery
This addresses a data gap for emergency managers needing to filter aerial photos after disasters, though it is incremental as it builds on prior datasets.
The authors tackled the problem of lacking training data for multi-label classification of low-altitude disaster imagery by presenting the LADI v2 dataset with about 10,000 annotated images, and they provided baseline classifiers that were compared to state-of-the-art models.
ML-based computer vision models are promising tools for supporting emergency management operations following natural disasters. Arial photographs taken from small manned and unmanned aircraft can be available soon after a disaster and provide valuable information from multiple perspectives for situational awareness and damage assessment applications. However, emergency managers often face challenges finding the most relevant photos among the tens of thousands that may be taken after an incident. While ML-based solutions could enable more effective use of aerial photographs, there is still a lack of training data for imagery of this type from multiple perspectives and for multiple hazard types. To address this, we present the LADI v2 (Low Altitude Disaster Imagery version 2) dataset, a curated set of about 10,000 disaster images captured in the United States by the Civil Air Patrol (CAP) in response to federally-declared emergencies (2015-2023) and annotated for multi-label classification by trained CAP volunteers. We also provide two pretrained baseline classifiers and compare their performance to state-of-the-art vision-language models in multi-label classification. The data and code are released publicly to support the development of computer vision models for emergency management research and applications.