# Urban Change Detection Using a Dual-Task Siamese Network and Semi-Supervised Learning

arXiv: https://arxiv.org/abs/2204.12202 · HTML: https://mlsanity.com/paper/2204.12202/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model](https://mlsanity.com/paper/1909.07726/) (2019)
- [The Multi-Temporal Urban Development SpaceNet Dataset](https://mlsanity.com/paper/2102.04420/) (2021)

## Built on by (most cited first, 10 of 10)

- [PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Models](https://mlsanity.com/paper/2412.04204/) (2024)
- [Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2205.13769/) (2022)
- [Made to Order: Discovering monotonic temporal changes via self-supervised video ordering](https://mlsanity.com/paper/2404.16828/) (2024)
- [Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integration](https://mlsanity.com/paper/2406.17458/) (2024)
- [Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwanda](https://mlsanity.com/paper/2303.08511/) (2023)
- [TSGAN: An Optical-to-SAR Dual Conditional GAN for Optical based SAR Temporal Shifting](https://mlsanity.com/paper/2401.00440/) (2023)
- [Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detection](https://mlsanity.com/paper/2409.15021/) (2024)
- [AdaSemiCD: An Adaptive Semi-Supervised Change Detection Method Based on Pseudo-Label Evaluation](https://mlsanity.com/paper/2411.07758/) (2024)
- [Enabling Advanced Land Cover Analytics: An Integrated Data Extraction Pipeline for Predictive Modeling with the Dynamic World Dataset](https://mlsanity.com/paper/2410.09135/) (2024)
- [GTPC-SSCD: Gate-guided Two-level Perturbation Consistency-based Semi-Supervised Change Detection](https://mlsanity.com/paper/2411.18880/) (2024)

## Abstract (arXiv)

In this study, a Semi-Supervised Learning (SSL) method for improving urban change detection from bi-temporal image pairs was presented. The proposed method adapted a Dual-Task Siamese Difference network that not only predicts changes with the difference decoder, but also segments buildings for both images with a semantics decoder. First, the architecture was modified to produce a second change prediction derived from the semantics predictions. Second, SSL was adopted to improve supervised change detection. For unlabeled data, we introduced a loss that encourages the network to predict consistent changes across the two change outputs. The proposed method was tested on urban change detection using the SpaceNet7 dataset. SSL achieved improved results compared to three fully supervised benchmarks.