Summary of Ladi V2: Multi-label Dataset and Classifiers For Low-altitude Disaster Imagery, by Samuel Scheele et al.
LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery
by Samuel Scheele, Katherine Picchione, Jeffrey Liu
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LADI v2 dataset is a curated collection of approximately 10,000 disaster images captured by the Civil Air Patrol (CAP) in response to federally-declared emergencies from 2015-2023. These images are annotated for multi-label classification and can be used to train machine learning-based computer vision models for situational awareness and damage assessment applications. The authors also provide two pretrained baseline classifiers and evaluate their performance on state-of-the-art vision-language models in multi-label classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The LADI v2 dataset is a collection of pictures taken from small planes after natural disasters. These images can help emergency managers make decisions about what to do next. However, there were no good ways to find the most important photos among all the ones taken. To solve this problem, researchers created a dataset with 10,000 images and had volunteers label them for different purposes. This will help develop computer vision models that can be used in emergency management. |
Keywords
» Artificial intelligence » Classification » Machine learning
