In the realm of remote sensing imagery, detecting changes is crucial for understanding environmental shifts and monitoring urban development. In this blog, we’ll explore how to implement the STAR and ChangeStar methods from the ICCV 2021 paper titled “Change is Everywhere: Single-Temporal Supervised Object Change Detection for High Spatial Resolution Remote Sensing Imagery.” This guide will help you set up the necessary environment, prepare datasets, and execute training and evaluation processes.

Introduction to Change Detection

Change detection is like a detective game where you compare two scenes and spot differences. Imagine looking at two photos of the same location taken at different times; you would notice changes in buildings, roads, or vegetation. Similarly, in remote sensing, we analyze satellite images to detect such variations over time. With the STAR and ChangeStar methods, the aim is to create robust detectors that can effectively identify these changes even with limited temporal supervision.

Getting Started

Installation

First, make sure you have EVer installed, which is a requirement for running the code.

• Run the following command to install it:

bash
pip install ever-beta==0.2.3

Requirements

• pytorch = 1.6.0
• python = 3.6

Prepare the Datasets

You will need the following datasets:

1. xView2 dataset (both the training and tier-3 sets)
2. LEVIR-CD dataset

Next, create soft links to these datasets:

bash
ln -s pathtoxView2 .xview2
ln -s pathtoLEVIR-CD .LEVIR-CD

Training and Evaluation

Now, it’s time to train and evaluate your models under different supervision settings.

Single-Temporal Supervision

bash
bash .scripts/train/xView2/r50_farseg_changemixin_symmetry.sh

Bitemporal Supervision

bash
bash .scripts/bisup_levircdr50_farseg_changemixin.sh

Troubleshooting

Here are some common issues you might encounter while setting up the project:

• Issue: Installation errors

Make sure you are using the correct versions of PyTorch and Python as mentioned in the requirements section.

• Issue: Dataset not found

Ensure that the paths to your datasets are correct when creating the soft links. You can verify the paths by listing the contents with ls.

• Issue: Execution halted

Check the script permissions. You might need to run chmod +x script_name.sh to make them executable.

• Issue: Sandbox environment issues

If you are working in a restricted environment, make sure that all dependencies are available and that you have the right permissions to execute the scripts.

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Conclusion

Implementing these methods will not only enhance your understanding of object change detection but also provide you with a strong foundation for future research in this area. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.