Open-Vocabulary vs Supervised Learning Methods for Post-Disaster Visual Scene Understanding

TL;DR

This paper compares supervised and open-vocabulary vision models for post-disaster aerial scene understanding, highlighting that supervised methods are more reliable when labels are available, especially for small objects and detailed boundaries.

Contribution

It provides a comprehensive evaluation of both approaches across multiple datasets, offering insights into their performance, failure modes, and practical trade-offs in disaster response scenarios.

Findings

Supervised training outperforms open-vocabulary models when labels are available.

Open-vocabulary models are less reliable for small objects and detailed scene boundaries.

Supervised methods are more effective in cluttered, ambiguous post-disaster imagery.

Abstract

Aerial imagery is critical for large-scale post-disaster damage assessment. Automated interpretation remains challenging due to clutter, visual variability, and strong cross-event domain shift, while supervised approaches still rely on costly, task-specific annotations with limited coverage across disaster types and regions. Recent open-vocabulary and foundation vision models offer an appealing alternative, by reducing dependence on fixed label sets and extensive task-specific annotations. Instead, they leverage large-scale pretraining and vision-language representations. These properties are particularly relevant for post-disaster domains, where visual concepts are ambiguous and data availability is constrained. In this work, we present a comparative evaluation of supervised learning and open-vocabulary vision models for post-disaster scene understanding, focusing on semantic segmentation and object detection across multiple datasets, including FloodNet+, RescueNet, DFire, and LADD. We examine performance trends, failure modes, and practical trade-offs between different learning paradigms, providing insight into their applicability for real-world disaster response. The most notable remark across all evaluated benchmarks is that supervised training remains the most reliable approach (i.e., when the label space is fixed and annotations are available), especially for small objects and fine boundary delineation in cluttered scenes.