# Overcoming Missing and Incomplete Modalities with Generative Adversarial Networks for Building Footprint Segmentation

arXiv: https://arxiv.org/abs/1808.03195 · HTML: https://mlsanity.com/paper/1808.03195/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images](https://mlsanity.com/paper/2102.11262/) (2021)
- [CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities](https://mlsanity.com/paper/2304.06485/) (2023)
- [Incomplete Multimodal Learning for Remote Sensing Data Fusion](https://mlsanity.com/paper/2304.11381/) (2023)
- [Multi-Modal Deep Learning for Multi-Temporal Urban Mapping With a Partly Missing Optical Modality](https://mlsanity.com/paper/2306.00640/) (2023)
- [Filling the Gaps: A Multitask Hybrid Multiscale Generative Framework for Missing Modality in Remote Sensing Semantic Segmentation](https://mlsanity.com/paper/2509.11102/) (2025)
- [Unveiling Incomplete Modality Brain Tumor Segmentation: Leveraging Masked Predicted Auto-Encoder and Divergence Learning](https://mlsanity.com/paper/2406.08634/) (2024)

## Abstract (arXiv)

The integration of information acquired with different modalities, spatial resolution and spectral bands has shown to improve predictive accuracies. Data fusion is therefore one of the key challenges in remote sensing. Most prior work focusing on multi-modal fusion, assumes that modalities are always available during inference. This assumption limits the applications of multi-modal models since in practice the data collection process is likely to generate data with missing, incomplete or corrupted modalities. In this paper, we show that Generative Adversarial Networks can be effectively used to overcome the problems that arise when modalities are missing or incomplete. Focusing on semantic segmentation of building footprints with missing modalities, our approach achieves an improvement of about 2% on the Intersection over Union (IoU) against the same network that relies only on the available modality.