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In the field of resource investigation, AI-based satellite remote sensing technology has deeply integrated into the main business and workflow of resource investigation.
Firstly, various satellite-based remote sensing data with meter-level and sub-meter-level resolution have been fully applied in land resource investigation and monitoring, geological mineral exploration, basic geographic information database updates, and other aspects.
Secondly, through intelligent processing and analysis of high-resolution remote sensing big data, we can quickly extract and discover comprehensive changes in national land resources, achieving high-precision dynamic monitoring of key areas such as residential, commercial, industrial land, forestry land, and agricultural land.
Currently, based on domestic high-resolution (GF), resource (ZY), environment (HJ), and Luojia No.1 satellite data, we have achieved accurate extraction of elements such as water bodies, roads, residential areas, commercial areas, and construction land in key areas, basically meeting the needs of resource investigation.
In terms of environmental monitoring, satellite remote sensing technology has been successfully applied to the monitoring of the atmosphere, water, soil, and vegetation ecological environment. For example,
In atmospheric pollution remote sensing monitoring, intelligent remote sensing image processing and interpretation technology have been successfully applied to detect polluting gases such as sulfur oxides and greenhouse gases like ozone, and to retrieve aerosol optical thickness.
In water pollution remote sensing monitoring, artificial intelligence technology has been applied to the quantitative retrieval of water quality parameters such as chlorophyll, plankton, suspended matter, and eutrophication indicators.
In ecological environment remote sensing retrieval, using artificial intelligence technology can quantitatively retrieve a series of ecological parameters such as leaf area index, biomass, vegetation coverage, vegetation height, vegetation water content, and land surface temperature.
In marine environmental monitoring, unsupervised and semi-supervised deep neural network methods have been applied to monitor the area of marine oil spills, pollution speed, and diffusion direction.
In disaster monitoring, artificial intelligence technology can achieve dynamic monitoring of typical natural disasters such as floods, fires, earthquakes, landslides, and tsunamis. For example,
During the disaster preparedness stage such as daily monitoring and early warning of disasters, satellite intelligent remote sensing technology can identify information such as potential disaster-prone environments, disaster-causing factors, and disaster-bearing bodies, and achieve disaster risk assessment and vulnerability assessment of disaster-bearing bodies through risk grading and regional division of disaster occurrence risks, providing a basis for disaster response and mitigation preparedness measures.
During the disaster emergency response stage, satellite intelligent remote sensing technology is applied to dynamic monitoring of the disaster itself and loss assessment of disaster-bearing bodies, providing important decision-making basis for comprehensive evaluation, secondary disaster risk warning, and disaster relief.
During the post-disaster recovery and reconstruction period, satellite intelligent remote sensing technology effectively supports the formulation of recovery and reconstruction planning in disaster areas, and conducts dynamic evaluation of the progress, benefits, and quality of recovery and reconstruction, providing scientific data references for the effectiveness of recovery and reconstruction and disaster mitigation facility construction.
In smart cities, intelligent satellite-based remote sensing technology is an important means to comprehensively and macroscopically perceive urban status, providing decision support for urban construction, environmental protection, and emergency rescue. For example,
In urban planning and management, combining satellite-based remote sensing with 5G communication, digital twins, and artificial intelligence technologies can build a precise, efficient, and real-time urban spatial planning and management "one map", achieving an intelligent urban monitoring and management system that integrates information from water conservancy, transportation, industry, agriculture, medical care, education, and more.
In smart urban transportation, the combination of spatial information and artificial intelligence technology can conveniently and efficiently identify road traffic operation conditions, providing technical support for traffic control and reducing traffic congestion.
In smart urban water conservancy, intelligent satellite-based remote sensing technology can be used to monitor urban impervious surfaces and water pollution conditions, predicting high-risk areas for floods and waterlogging.
In agriculture, remote sensing image recognition and quantitative retrieval technology can be used to monitor the planting area, type, and spatial distribution of crops, monitor the growth of crops, classify and estimate crop yields, identify and predict crop diseases and pests, and monitor soil moisture and surface evapotranspiration.
In forestry, satellite-based remote sensing has been successfully applied to the monitoring of forestry pests and diseases, surveillance and protection of forestry resources, and estimation of forest biomass.
In aquaculture, satellite-based remote sensing is used to monitor fisheries, oversee foreign fisheries, and monitor the eutrophication of fishery water bodies.
