ML-based computer vision models are promising tools for supporting emergency management operations following natural disasters. Imagery 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 in effectively utilizing this data due to the difficulties in finding the most relevant imagery among the tens of thousands of images that may be taken after an event. Despite this promise, 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 by the Civil Air Patrol (CAP) in response to over 50 disaster events (2015-2023) from over 30 US states and territories 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.