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Building and change detection in satellite images has become an essential tool for urban planning, disaster response, and damage assessment. Traditionally, computer vision models were trained only with pre-disaster images, assuming that post-disaster images degraded performance due to the presence of destroyed buildings. However, recent research shows that it is possible to leverage both sources —pre and post disaster— using Siamese network architectures combined with transformers. A notable example is SiamixFormer, a model that employs two hierarchical encoders based on transformers and a temporal fusion module that uses queries generated from the pre-disaster image and key-value pairs from the post-disaster image. This approach preserves large receptive fields and achieves superior performance on datasets such as xBD, WHU, LEVIR-CD, and CDD, for both building detection and change detection.
SiamixFormer's innovation lies in its ability to integrate the temporal dimension into feature fusion, something conventional convolutional networks do not handle as effectively. By using transformers, the model extracts long-range relationships and better understands the transformations between before and after a catastrophic event. This advancement is not only relevant in academia but also opens opportunities in custom software development for government agencies, insurance companies, and infrastructure management firms. At Q2BSTUDIO, we offer custom applications that integrate artificial intelligence to analyze satellite images and generate early warnings, automating critical processes in damage assessment.
The practical application of models like SiamixFormer goes beyond building detection. It can be scaled to domains such as crop monitoring, change detection in linear infrastructure, or surveillance of conflict zones. Implementing these systems at an enterprise level requires a robust and secure infrastructure. Therefore, at Q2BSTUDIO, we combine aws and azure cloud services with cutting-edge cybersecurity, ensuring that sensitive data —such as high-resolution images or damage reports— is protected. Additionally, our business intelligence services solutions allow visualizing key metrics derived from image analysis in interactive dashboards (with power bi), facilitating strategic decision-making.
Another key aspect is the ability to create autonomous AI agents that, trained with models similar to SiamixFormer, can continuously monitor changes in the terrain and notify response teams. These agents can run in cloud environments and scale on demand, representing a qualitative leap over manual processes. At Q2BSTUDIO, we develop ai for businesses that transform workflows in areas such as emergency management, urban planning, and humanitarian logistics. The integration of custom software with cutting-edge models allows organizations to stay ahead of disasters and optimize their resources.
In summary, the evolution towards Siamese architectures with transformers represents a milestone in remote sensing, and its practical implementation requires a comprehensive technological strategy. At Q2BSTUDIO, we accompany our clients throughout the entire cycle: from model design to deployment in cloud infrastructure, including cybersecurity and data visualization. The combination of artificial intelligence, aws and azure cloud services, and business intelligence services enables building agile, accurate, and future-ready systems.
