LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery
Keywords: computer vision, dataset, disaster, aerial imagery, multilabel classification, damage assessment
TL;DR: We contribute a dataset of 10k low-altitude aerial images of areas impacted by disasters labeled for multi-label classification and two baseline models.
Abstract: 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 100 federal emergency declarations (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.
Supplementary Material: zip
Primary Area: datasets and benchmarks
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 7006
Loading