Alibaba DAMO Academy releases the industry's first large-scale remote sensing AI model, claiming to be able to identify nearly 100 types of land object classifications

News on October 20, according to the official public account of DAMO Academy, Alibaba DAMO Academy today released the industry's first remote sensing AI large model (AIE-SEG) , claiming to be "the first in remote sensing The field realizes the unification of image segmentation tasks" and "one model realizes the rapid extraction of 'zero samples of all things'", and can identify nearly a hundred types of remote sensing land objects such as farmland, water, and buildings, and can also classify them based on the user's interactive feedback. Automatically tune recognition results.

It is reported that remote sensing technology is mainly used in urban planning, farmland protection, emergency disaster relief and other industry applications. With the support of AI, relevant remote sensing technology can analyze satellite capture content and historical meteorological data, thereby assisting urban operations. , farmland protection, emergency disaster relief and other industry applications.

This site summarizes the features of this large remote sensing model as follows:

• Supports multi-modal interaction. For example, if you enter "Extract farmland from the image", it will automatically identify all the fields. Select target

• ## Supports any surface target recognition and establishes multi-level semantic labels System

▲ Picture source DAMO official public account

• supports satellites and unmanned aerial vehicles Extraction of all elements of machine images

##▲ Picture source DAMO official public account

Support Interactive result correction, such as mistakenly extracting "water areas" when identifying "paddy fields", can be corrected through manual operations

▲ Picture source DAMO official Public account

supports general and multi-category change detection

▲ Picture source DAMO official The public account

officially stated that in some specific scenarios, compared with the traditional remote sensing model, the accuracy of instance extraction can be increased by 25%, and the accuracy of change detection can be increased by 30%. .

Damo Academy also claimed that this large remote sensing AI model provides "out-of-the-box" API calling services, and users can customize different remote sensing AI interpretation functions according to their needs. Such as "water extraction", "cultivated land change monitoring", "photovoltaic identification", etc.

This will allow AI to further penetrate into the fields, greatly improving the analysis efficiency of remote sensing applications such as disaster prevention, natural resource management, and agricultural yield estimation.

At present, this AI model has been applied in the industry. For example, the Shandong Provincial Land Surveying and Mapping Institute and Alibaba Damo Institute cooperated to use a remote sensing AI large model to monitor the growth of winter wheat, with a recognition accuracy of 90% % or more, effectively improving the efficiency of winter wheat remote sensing interpretation, helping agricultural managers better predict grain yield and improve agricultural production efficiency.

The National Institute of Natural Disaster Prevention and Control also uses this model to identify landslides and collapsed buildings. In the test of remote sensing images of historical natural disaster areas, it only takes ten minutes. It can extract disaster information, which is dozens of times more efficient than manual identification, providing efficient and accurate remote sensing analysis support for scientific disaster relief.

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