mlsanityData to 7 Oct 2026

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

arXiv 2204.12202 ↗First posted 26 Apr 2022cs.CVcited by 10

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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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 26 Apr 2022.

Builds on

4 of 8 references.

References · 4

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018Fully Convolutional Siamese Networks for Change Detectioncited by 305
  3. 2019Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Modelcited by 84
  4. 2021The Multi-Temporal Urban Development SpaceNet Datasetcited by 62

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 7.

10 papers.

  1. 2024PANGAEA: A Global and Inclusive Benchmark for Geospatial Foundation Modelscited by 62
  2. 2022Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detectioncited by 16
  3. 2024Made to Order: Discovering monotonic temporal changes via self-supervised video orderingcited by 5
  4. 2024Continuous Urban Change Detection from Satellite Image Time Series with Temporal Feature Refinement and Multi-Task Integrationcited by 5
  5. 2023Mapping Urban Population Growth from Sentinel-2 MSI and Census Data Using Deep Learning: A Case Study in Kigali, Rwandacited by 2
  6. 2023TSGAN: An Optical-to-SAR Dual Conditional GAN for Optical based SAR Temporal Shiftingcited by 2
  7. 2024Cross Branch Feature Fusion Decoder for Consistency Regularization-based Semi-Supervised Change Detectioncited by 2
  8. 2024AdaSemiCD: An Adaptive Semi-Supervised Change Detection Method Based on Pseudo-Label Evaluationcited by 2
  9. 2024Enabling Advanced Land Cover Analytics: An Integrated Data Extraction Pipeline for Predictive Modeling with the Dynamic World Datasetcited by 0
  10. 2024GTPC-SSCD: Gate-guided Two-level Perturbation Consistency-based Semi-Supervised Change Detectioncited by 0