# SCA-Net: Spatial-Contextual Aggregation Network for Enhanced Small Building and Road Change Detection

arXiv: https://arxiv.org/abs/2602.09529 · HTML: https://mlsanity.com/paper/2602.09529/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 26 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 19 of 19)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay](https://mlsanity.com/paper/1803.09820/) (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)
- [Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis](https://mlsanity.com/paper/2403.19646/) (2024)
- [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025)
- [Referring Change Detection in Remote Sensing Imagery](https://mlsanity.com/paper/2512.11719/) (2025)

## Abstract (arXiv)

Automated change detection in remote sensing imagery is critical for urban management, environmental monitoring, and disaster assessment. While deep learning models have advanced this field, they often struggle with challenges like low sensitivity to small objects and high computational costs. This paper presents SCA-Net, an enhanced architecture built upon the Change-Agent framework for precise building and road change detection in bi-temporal images. Our model incorporates several key innovations: a novel Difference Pyramid Block for multi-scale change analysis, an Adaptive Multi-scale Processing module combining shape-aware and high-resolution enhancement blocks, and multi-level attention mechanisms (PPM and CSAGate) for joint contextual and detail processing. Furthermore, a dynamic composite loss function and a four-phase training strategy are introduced to stabilize training and accelerate convergence. Comprehensive evaluations on the LEVIR-CD and LEVIR-MCI datasets demonstrate SCA-Net's superior performance over Change-Agent and other state-of-the-art methods. Our approach achieves a significant 2.64% improvement in mean Intersection over Union (mIoU) on LEVIR-MCI and a remarkable 57.9% increase in IoU for small buildings, while reducing the training time by 61%. This work provides an efficient, accurate, and robust solution for practical change detection applications.