# Building and Road Segmentation Using EffUNet and Transfer Learning Approach

arXiv: https://arxiv.org/abs/2307.03980 · HTML: https://mlsanity.com/paper/2307.03980/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Building extraction with vision transformer](https://mlsanity.com/paper/2111.15637/) (2021)
- [SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving](https://mlsanity.com/paper/2109.07701/) (2021)
- [Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networks](https://mlsanity.com/paper/1802.01445/) (2018)
- [DDU-Net: Dual-Decoder-U-Net for Road Extraction Using High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2201.06750/) (2022)
- [PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images](https://mlsanity.com/paper/2204.07243/) (2022)
- [Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training](https://mlsanity.com/paper/2205.08416/) (2022)
- [Automatic Pixelwise Object Labeling for Aerial Imagery Using Stacked U-Nets](https://mlsanity.com/paper/1803.04953/) (2018)

## Abstract (arXiv)

In city, information about urban objects such as water supply, railway lines, power lines, buildings, roads, etc., is necessary for city planning. In particular, information about the spread of these objects, locations and capacity is needed for the policymakers to make impactful decisions. This thesis aims to segment the building and roads from the aerial image captured by the satellites and UAVs. Many different architectures have been proposed for the semantic segmentation task and UNet being one of them. In this thesis, we propose a novel architecture based on Google's newly proposed EfficientNetV2 as an encoder for feature extraction with UNet decoder for constructing the segmentation map. Using this approach we achieved a benchmark score for the Massachusetts Building and Road dataset with an mIOU of 0.8365 and 0.9153 respectively.