# Building Damage Mapping with Self-PositiveUnlabeled Learning

arXiv: https://arxiv.org/abs/2111.02586 · HTML: https://mlsanity.com/paper/2111.02586/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 4 Nov 2021.

## Builds on (most cited first, 12 of 12)

- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training](https://mlsanity.com/paper/2006.11280/) (2020)
- [Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks](https://mlsanity.com/paper/1910.06444/) (2019)
- [RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery](https://mlsanity.com/paper/2004.07312/) (2020)
- [Detecting natural disasters, damage, and incidents in the wild](https://mlsanity.com/paper/2008.09188/) (2020)
- [Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping](https://mlsanity.com/paper/2009.06200/) (2020)
- [Assessing out-of-domain generalization for robust building damage detection](https://mlsanity.com/paper/2011.10328/) (2020)
- [An Attention-Based System for Damage Assessment Using Satellite Imagery](https://mlsanity.com/paper/2004.06643/) (2020)
- [Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques](https://mlsanity.com/paper/2011.14004/) (2020)
- [Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery](https://mlsanity.com/paper/2010.14014/) (2020)
- [Multi-class segmentation under severe class imbalance: A case study in roof damage assessment](https://mlsanity.com/paper/2010.07151/) (2020)

## Built on by (most cited first, 2 of 2)

- [Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets](https://mlsanity.com/paper/2205.15688/) (2022)
- [Noisy-Pair Robust Representation Alignment for Positive-Unlabeled Learning](https://mlsanity.com/paper/2510.01278/) (2025)

## Abstract (arXiv)

Humanitarian organizations must have fast and reliable data to respond to disasters. Deep learning approaches are difficult to implement in real-world disasters because it might be challenging to collect ground truth data of the damage situation (training data) soon after the event. The implementation of recent self-paced positive-unlabeled learning (PU) is demonstrated in this work by successfully applying to building damage assessment with very limited labeled data and a large amount of unlabeled data. Self-PU learning is compared with the supervised baselines and traditional PU learning using different datasets collected from the 2011 Tohoku earthquake, the 2018 Palu tsunami, and the 2018 Hurricane Michael. By utilizing only a portion of labeled damaged samples, we show how models trained with self-PU techniques may achieve comparable performance as supervised learning.