Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.
| Date | Stars |
|---|---|
| 2026-07-24 | 909 |
| 2026-07-25 | 909 |
| 2026-07-28 | 909 |
| 2026-07-30 | 909 |
| 2026-07-31 | 909 |
| 2026-08-06 | 908 |
Today
-1 stars today
This week
-1 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges
## 1. Introduction
Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.
<center>Figure 1. General schematic diagram of change detection.</center>
## 2. Implementation process
Figure 2 provide a general implementation process of AI-based change detection, but the structure of the AI model is diverse and needs to be well designed according to different application situations and the training data. It is worth mentioning that existing mature frameworks such as <a href="https://www.tensorflow.org/" target="_blank">TensorFlow</a>, <a href="https://keras.io/" target="_blank">Keras</a>, <a href="https://pytorch.org/" target="_blank">Pytorch</a>, and <a href="https://caffe.berkeleyvision.org/" target="_blank">Caffe</a>, help researchers more easily realize the design, training, and deployment of AI models, and their development documents provide detailed introductions.
<center>Figure 2. Implementation process of AI-based change detection (black arrows indicate workflow and red arrow indicates an example).</center>
### 2.1 Available codes for AI-based methods
<table>
<caption>Table 1. A list of available codes for AI-based change detection methods.</caption>
<tr>
<th>Methods</th>
<th>Keywords</th>
<th>Publication</th>
<th>(Re-)Implementation</th>
</tr>
<tr>
<td>SRCDNet</td>
<td>CNN; Siamese; Attention; Super-resolution; Optical RS</td>
<td>Super-resolution-based change detection network with stacked attention module for images with different resolutions, TGRS, 2021. [<a href="https://doi.org/10.1109/TGRS.2021.3091758" target="_blank">paper</a>], [<a href="https://github.com/liumency/SRCDNet" target="_blank">code, dataset</a>]</td>
<td>Pytorch 1.2</td>
</tr>
<tr>
<td>ESCNet</td>
<td>CNN; Siamese; Superpixel; Optical RS</td>
<td>An End-to-End superpixel-enhanced change detection network for Very-High-Resolution remote sensing images. TNNLS, 2021. [<a href="https://doi.org/10.1109/TNNLS.2021.3089332" target="_blank">paper</a>], [<a href="https://github.com/Bobholamovic/ESCNet" target="_blank">code</a>]</td>
<td>Pytorch 1.3</td>
</tr>
<tr>
<Excerpt of 76,800 characters
Read on GitHub
14
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:16af3fd7116cb395, topic:deep-learning, topic:pytorch, topic:tensorflow