Let AI sink into the fields and large models open a new chapter in remote sensing applications

Let AI sink into the fields and large models open a new chapter in remote sensing applications

For a long time, natural resources have been called the “first” of remote sensing industries. However, with the rapid increase in the number of remote sensing images, the problems of insufficient clarity of satellite images and insufficient supply of interpretation capabilities have become increasingly prominent. The "combination" of "AI remote sensing" came into being. Through artificial intelligence, the depth of utilization of existing data can be greatly improved, and the ability to translate remote sensing information can be enhanced to output more refined and accurate information. The result is even more intuitive.

With the continuous advancement of artificial intelligence technology, remote sensing technology is also developing in a smarter and more efficient direction. So, what kind of sparks can AI-enabled remote sensing technology create? How will it “subvert” the traditional remote sensing industry? Today, please follow "China Science and Technology Information" to find out.

Only AI can realize real satellite remote sensing applications?

Remote sensing technology is widely used in the national economy and people's livelihood, such as in-depth applications in urban operations, natural resource census, vegetation classification, crop monitoring and environmental monitoring. What users who use remote sensing technology want to obtain from remote sensing data is actually information, and this is also the biggest bottleneck faced by remote sensing applications: the bottleneck from data extraction and interpretation to information application. More specifically, the problem can be summarized as a lack of precise data, calculations not fast enough, and analysis not deep enough.

More and more people are beginning to realize that truly effective satellite remote sensing applications can only be achieved by relying on artificial intelligence technology. then. The "combination" of "AI remote sensing" came into being. Through artificial intelligence, the depth of utilization of existing data can be greatly improved, and the translation ability of remote sensing information can be strengthened to output more refined and accurate results, and even Give more vivid and intuitive results.

With the empowerment of AI, agricultural satellites "take" "photos" of the ground in space. Based on these image data, combined with meteorological conditions, etc., the algorithm accurately "calculates" the growth status of crops. , provide reference for local governments and farmers. This is a typical scenario in the field of digital agriculture empowered by AI remote sensing. When farming, you no longer have to passively "look at the sky to eat". You can take proactive actions based on the analyzed data. Say goodbye to the hard work mode of facing the loess and back to the sky, and be able to "in your heart" "There is a record" and relax.

In fact, AI remote sensing also has a wide range of application prospects, such as land cover classification, terrain analysis, urban planning, agricultural monitoring, natural resource management and other fields.

Remote sensing large model allows AI to sink into the fields

As a data-intensive business, remote sensing is very sensitive to the execution efficiency and cost of computing resources, from the interpretation and analysis of massive remote sensing images to the training and reasoning of the underlying AI models. Especially as the demand for fast response and high-resolution remote sensing applications continues to increase in all walks of life, AI computing power has become a major obstacle to the development of remote sensing AI. Therefore, this has also stimulated the intensive development of the AI ​​remote sensing large model industry.

What are the benefits of this large model? It avoids the phenomenon of "reinventing the wheel" by applying large-scale data pre-training. In the past, AI applications, including AI remote sensing, were a single innovation process. Each time to solve a specific problem in a scene, you needed to "do it all over again" starting from basic data and algorithms, just like small workshop-style production; now, large-scale The emergence of the model, on the basis of general capabilities, enterprises or developers can "produce" applications that are qualified for specific scenario tasks as long as they fine-tune based on the pre-trained model, just like the "industrial mass production" of ordinary commodities, efficiently By producing high-quality remote sensing AI applications, the entire field will achieve simple and efficient development.

It is foreseeable that with the paradigm innovation brought by large AI models, AI remote sensing technology is also expected to "sink" into more subdivided scenarios in the agricultural industry at lower cost and higher efficiency, helping Digital agricultural technology upgrade and promotion.

Intensive announcements by universities and enterprises indicate that the future of the industry can be expected

From the perspective of industrial dynamics, more and more high-tech companies and scientific research institutions have begun to "increase" large AI remote sensing models, and have made some phased progress and put them into use in the remote sensing industry.

For example, the Shangtang AI remote sensing large model is based on a general visual large model. It has high generalization capabilities for different land species, different image types, different image times and spectral bands, and has advanced land object interpretation capabilities and The generative patch effect is comparable to manual annotation. It has been widely used in the fields of cropping industry monitoring, non-agricultural and non-grain monitoring, cultivated land use control, agricultural-related credit, and agricultural-related insurance; the Aerospace Information Innovation Institute of the Chinese Academy of Sciences and the technical team of Beijing Shengteng Artificial Intelligence Ecological Innovation Center launched "Aerospace The "Lingmo" large model has the ability to understand and restore remote sensing data, and can represent the common semantic space of cross-modal remote sensing data. Future applications will not only be limited to three-dimensional reconstruction and other fields, but may be further extended to more industries such as land and resources, transportation, water conservancy, etc., providing a complete set of solutions for the integrated application of space, space and ground; the aerospace ambition creates a large visual model of "Tianquan" for Multi-modal remote sensing data aims to solve the limitations of sample annotation and model generalization under the existing "AI remote sensing" business model, and is committed to building an integrated intelligent remote sensing ecosystem of "segmentation, detection, and generation" to empower national defense security, Land resources, transportation and water conservancy and other application fields.

Although AI remote sensing large models have many advantages, they will still face some challenges in future industrial application directions. Processing and analyzing massive amounts of remote sensing data quickly and efficiently remains a difficult problem. In addition, how to protect the security and privacy of remote sensing data is also a problem that needs to be solved.

This requires further solving the problems of artificial intelligence methods in automatic interpretation of remote sensing. It is necessary to continue to expand the sample database and increase diversity and regional samples. On the other hand, it is necessary to design a deep learning neural network dedicated to remote sensing. Integrating spectral information and geoscience knowledge into the network enables it to effectively solve difficult problems such as classification of physical geographical elements and objects. This undoubtedly requires relying on the development of my country's core technologies and infrastructure for safe and independent artificial intelligence, assisting scientific research innovation breakthroughs such as intelligent remote sensing interpretation research, and achieving industrial ecological prosperity.

In short, With the continuous improvement of artificial intelligence technology and computing power, the application prospects of AI remote sensing large models are still very broad. In the future, we can expect that large AI remote sensing models will play an important role in more fields and make greater contributions to the sustainable development of human society.

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