https://github.com/liaochengcsu/BCE-Net

A reliable building footprints change extraction network based on historical map and up-to-date images
https://github.com/liaochengcsu/BCE-Net

Last synced: 3 months ago
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A reliable building footprints change extraction network based on historical map and up-to-date images

• Host: GitHub
• URL: https://github.com/liaochengcsu/BCE-Net
• Owner: liaochengcsu
• License: gpl-3.0
• Created: 2023-02-12T07:40:38.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-22T08:24:45.000Z (11 months ago)
• Last Synced: 2024-07-12T12:38:07.388Z (4 months ago)
• Language: Python
• Size: 23.7 MB
• Stars: 23
• Watchers: 3
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-remote-sensing-change-detection - **SI-BU dataset**

README

        
# BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning

A reliable building footprint change extraction network based on historical maps and up-to-date images. It recognized the multi-kinds of instance-level change for the building combined with the latest images and historical footprints. We provided the dataset named SI-BU for further research.

## DataSets

+ The SI-BU Dataset (latest-temporal images and change states{0,1,2,3}): Download at [SI-BU-Baiduyun](https://pan.baidu.com/s/1CNdlv51cAu2tXqRHPRftMA?pwd=2024) with extract code: 2024.  It contains 3604 and 1328 sliced tiles for training and test datasets.

+ The SI-BU Dataset (bi-temporal images and change masks{0,255}): Download at [SI-BU-Baiduyun](https://pan.baidu.com/s/1kC7QEAJRSwU-KZv-sUlG7g?pwd=2024) with extract code: 2024.  It contains 3593 tiles for the training set since we deleted some incorrect samples.

+ 


+ The modified WHU-CD Dataset: Download at [WHU-CD-Baiduyun](https://pan.baidu.com/s/1fTjBomPH0gFAHUlP5k9wqQ?pwd=2024) with extract code: 2024. It contains 1260 and 690 sliced tiles same as the [Offcial Webset](http://gpcv.whu.edu.cn/data/building_dataset.html)  



## Abstract

Automatic and periodic recompiling of building databases with up-to-date high-resolution images has become a critical requirement for rapidly developing urban environments. However, the architecture of most existing approaches for change extraction attempts to learn features related to changes but ignores objectives related to buildings. This inevitably leads to the generation of significant pseudo-changes, due to factors such as seasonal changes in images and the inclination of building fa¸cades. To alleviate the above-mentioned problems, we developed a contrastive learning approach by validating historical building footprints against single up-to-date remotely sensed images. This contrastive learning strategy allowed us to inject the semantics of buildings into a pipeline for the detection of changes, which is achieved by increasing the distinguishability of features of buildings from those of non-buildings. In addition, to reduce the effects of inconsistencies between historical building polygons and buildings in up-to-date images, we employed a deformable convolutional neural network to learn offsets intuitively. In summary, we formulated a multi-branch building extraction method that identifies newly constructed and removed buildings, respectively. To validate our method, we conducted comparative experiments using the public Wuhan University building change detection dataset and a more practical dataset named SI-BU that we established. Our method achieved F1 scores of 93.99% and 70.74% on the above datasets, respectively. Moreover, when the data of the public dataset were divided in the same manner as in previous related studies, our method achieved an F1 score of 94.63%, which surpasses that of the state-of-the-art method. 



## Method



BCE-Net consists of four parts: a pre-trained encoder for extracting robust multi-level features; multi-task segmentation branches for extraction of newly constructed, removed, and existing buildings; a DCN-based transform module for consistent adaptive adjustment of features; and a building instance-constrained contrastive learning module for discriminating feature optimization.

## Test

+ Create an environment according to requirements.txt

+ Build the [DCNv2](https://github.com/CharlesShang/DCNv2/tree/master) (Deformable Convolutional Networks V2)

+ Download the trained weights at [Weights-Baiduyun](https://pan.baidu.com/s/1LjhSh3ijoxzwn8dei8Z-4g) with extract code: wyxv

+ Prepare the data and run the testXX.py， we provided a detailed description in the comments

## Results







## Reference

https://github.com/CharlesShang/DCNv2/tree/master