# Multi3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

arXiv: https://arxiv.org/abs/1812.01756 · HTML: https://mlsanity.com/paper/1812.01756/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 22 of 22)

- [Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks](https://mlsanity.com/paper/2112.01715/) (2021)
- [RescueNet: A High Resolution UAV Semantic Segmentation Benchmark Dataset for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2202.12361/) (2022)
- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images](https://mlsanity.com/paper/2205.02049/) (2022)
- [VQA-Aid: Visual Question Answering for Post-Disaster Damage Assessment and Analysis](https://mlsanity.com/paper/2106.10548/) (2021)
- [Attention Based Semantic Segmentation on UAV Dataset for Natural Disaster Damage Assessment](https://mlsanity.com/paper/2105.14540/) (2021)
- [Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing Applications](https://mlsanity.com/paper/2301.01200/) (2022)
- [S4: Self-Supervised Sensing Across the Spectrum](https://mlsanity.com/paper/2405.01656/) (2024)
- [DeepDamageNet: A two-step deep-learning model for multi-disaster building damage segmentation and classification using satellite imagery](https://mlsanity.com/paper/2405.04800/) (2024)
- [Self-Supervised Learning for Building Damage Assessment from Large-scale xBD Satellite Imagery Benchmark Datasets](https://mlsanity.com/paper/2205.15688/) (2022)
- [Road Extraction with Satellite Images and Partial Road Maps](https://mlsanity.com/paper/2303.12394/) (2023)
- [Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks](https://mlsanity.com/paper/2506.04285/) (2025)
- [GeoCrossBench: Cross-Band Generalization for Remote Sensing](https://mlsanity.com/paper/2511.02831/) (2025)
- [Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India](https://mlsanity.com/paper/2402.13796/) (2024)
- [Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery](https://mlsanity.com/paper/2506.06667/) (2025)
- [Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning](https://mlsanity.com/paper/2405.17734/) (2024)
- [EvaNet: Elevation-Guided Flood Extent Mapping on Earth Imagery (Extended Version)](https://mlsanity.com/paper/2404.17917/) (2024)
- [The Potential of Copernicus Satellites for Disaster Response: Retrieving Building Damage from Sentinel-1 and Sentinel-2](https://mlsanity.com/paper/2511.05461/) (2025)
- [Extracting Built Environment Features for Planning Research with Computer Vision: A Review and Discussion of State-of-the-Art Approaches](https://mlsanity.com/paper/2201.12693/) (2022)
- [Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods](https://mlsanity.com/paper/2306.06074/) (2023)
- [Flood Data Analysis on SpaceNet 8 Using Apache Sedona](https://mlsanity.com/paper/2404.18235/) (2024)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)

## Abstract (arXiv)

We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite imagery-based flood maps, crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our model allows for rapid and accurate post-disaster damage assessment and can be used by governments to better coordinate medium- and long-term financial assistance programs for affected areas. The network consists of multiple streams of encoder-decoder architectures that extract spatiotemporal information from medium-resolution images and spatial information from high-resolution images before fusing the resulting representations into a single medium-resolution segmentation map of flooded buildings. We compare our model to state-of-the-art methods for building footprint segmentation as well as to alternative fusion approaches for the segmentation of flooded buildings and find that our model performs best on both tasks. We also demonstrate that our model produces highly accurate segmentation maps of flooded buildings using only publicly available medium-resolution data instead of significantly more detailed but sparsely available very high-resolution data. We release the first open-source dataset of fully preprocessed and labeled multiresolution, multispectral, and multitemporal satellite images of disaster sites along with our source code.