The escalating frequency and severity of natural disasters pose significant threats to human health, infrastructure, and ecosystems. The timely and accurate provision of information has the potential to revolutionize disaster management, particularly in accessing visual data for rapid response and recovery. Unmanned aerial systems (UAS) with affordable sensors offer opportunities to collect extensive high-resolution data, especially from inaccessible areas. However, the challenge lies in effectively analyzing these large datasets.

The RescueNet initiative contributes high-resolution UAS imagery with detailed classification, semantic segmentation, and visual question-answering annotations. This challenge will have several tracks including semi-supervised semantic segmentation and VQA.

Dataset Details

The data is collected with a small UAS platform, DJI Mavic Pro quadcopters, after Hurricane Michael. The whole dataset has 4494 images, divided into training (~80%), validation (~10%), and test (~10%) sets. For Track 1- Semi-supervised Semantic Segmentation, in the training set, we have around 900 labeled images (~25% of the training set) and around 2695 unlabeled images (~75% of the training set ).

For Track 2- Visual Question Answering (VQA): In total 100,000 questions were generated from 4300 images (RescueNet-VQA dataset). Each image is associated with multiple questions depending on the Question Types. In the training set, we have around 70,000 Image-Question (QA) pairs. The rest of the data sample will be further considered for model evaluation. Both training and evaluation datasets are mutually exclusive. 

RescueNet Challenge Tracks

This challenge mainly offers two tracks for post-disaster damage assessment on the RescueNet dataset. The first track focuses on Semantic Segmentation, while the second track focuses on Visual Question Answering.

Track 1:

Semi-Supervised Semantic Segmentation: The semantic segmentation labels include: 1) Background, 2) Water, 3)Building No Damage, 4) Building Minor Damage, 5) Building Major Damage, 6) Building Total Destruction, 7) Road-Clear, 8) Road-Blocked, 9) Vehicle, 10) Tree, 11) Pool. Only a small portion of the training images have their corresponding masks available.

Track 2:

For the Visual Question Answering (VQA) task, we will provide QA pairs. These questions can be divided into the 9 categories: Simple Counting (SC), Complex Counting (CC), Building Condition Recognition (BCR), Road Condition Recognition (RCR), Level of Damage (LOD), Risk Assessment (RA), Density Estimation (DE), Positional (POS) and Change Detection (CD).

How to Participate

Evaluation Criteria