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【2021 Application Example】 AI Analysis Cloud Service Platform for Remote Sensing Big Data Enables the Smooth Application of Satellite Remote Sensing Images

編輯群2021-11-28
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Although satellite remote sensing images can make all surface objects visible, it still requires a lot of time and manpower to be truly applied to the industry. In order to effectively solve the problems that customers face in digesting huge amounts of image data and eliminate technical obstacles for cross-domain users to process satellite remote sensing images, ThinkTron has developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" as a new beginning for cross-domain AI applications for spatial information.

In recent years, in response to the impact of industrial globalization, Taiwan's agriculture has continued to transition towards technology-based and higher quality, improving the yield and quality of crops by solving problems, such as microclimate impacts and pest and disease control. The demand of agriculture on images has expanded endlessly to accurately grasp the growing environment of crops. In the early years when UAVs (unmanned aerial vehicles) were not yet popular, manual field surveys were the most basic but most labor-intensive work. With the emergence of UAV drones, aerial photography operations might not be difficult, but the range that can be photographed is limited. Furthermore, surveying expertise is required to accurately capture spatial information. At this time, the use of satellite remote sensing data may break away from the past imagination of using image data.

Taiwan Space Agency (TASA) ODC data warehouse services

▲ Taiwan Space Agency (TASA) ODC data warehouse services

In the past ten years, with the breakthrough of modern satellite remote sensing application technology, Digital Earth has become a new trend in global data collection. Countries have developed data cube image storage technology, and the development of smart agriculture has become one of the largest image users. Determining planting distribution is the first step in understanding crop yields. Free satellite remote sensing images, powerful data warehousing support, and the team's robust image recognition technology are important supports for accelerating agricultural transformation.

Using satellite remote sensing image data can accelerate the development of smart agriculture.

However, in the past, it was difficult to extract large-area crop distribution through satellite remote sensing images, not to mention the cost. If you wanted to use free information, you had to visit all websites of international space agencies, look through the wide variety of satellite specifications, and carefully evaluate the sensor specifications, image resolution, and revisit cycle. After finding suitable images, you had to look at them one by one to filter the ones you need. Next is downloading dozens of images that are often several hundreds of Megabytes (MB) each, which might exceed the capacity of your computer.

Also, after overcoming image access and preparing data, you must then start to confirm the downloaded image products and which bands you want, because the image you see is not just an image file (.jpg or .png), but rather complex multi-spectral information, attribute fields and coordinate information. It takes a lot of effort just to confirm the correct information. Facing GIS software packages with complex functions is the start of another trouble. The complex image pre-processing process and the inflexible machine learning package greatly reduce the efficiency of analyzing data. After finally getting the results of crop identification, you might find that the best time for using map information may have already passed. The above-mentioned complex and time-consuming satellite image processing problems are precisely the pain points of the market. ThinkTron expanded from traditional machine learning to modern deep learning applications, and developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" under the GeoAI framework, breaking through the constraints of details in the spatial information for customers.

Differences between the process before and after introducing the AI analysis cloud service platform

▲Differences between the process before and after introducing the AI analysis cloud service platform

ThinkTron said that Taiwan's ODC (Open Data Cube) system has been completed and began providing services after years of efforts from the Taiwan Space Agency (TASA), formally becoming aligned with international trends. The powerful warehousing technology allows users to easily capture and use image data of a specific time and spatial range according to their needs. The warehouse stores multiple satellite image resources from international space agencies, including the ESA's Sentinel-1 (one image every 6 days), Sentinel-2 (one image every 6 days), USGS's Landsat-7 (one image every 16 days), Landsat-8 (one image every 16 days), and the domestic Formosat-2 (one image every day) and Formosat-5 (one image every 2 days).

ThinkTron develops satellite image recognition tools based on Python

Breaking free from the limitations of GIS (Geographic Information System) software packages, ThinkTron integrated GDAL (Geospatial Data Abstraction Library) based on Python, and considered computing efficiency and parallel processing when developing all tools required for satellite image processing and image recognition modeling, including coordinate system and data format conversion, grid and vector data interaction, and data intra-difference and normalization. All of the tools are designed with AI applications in mind, and some commonly used tools are packaged into an open source package under the name TronGisPy to benefit the technical community. ThinkTron utilized the team's understanding of satellite remote sensing images and the collected tagged data (crop distribution maps) to preset the image recognition modeling process, the required training data specifications, and dataset definitions. This is imported into the machine learning (LightGBM) or deep learning (CNN) framework that was completed in advance, and the entire training process to be performed in the Web GIS interface, providing users with partial flexibility to freely filter images, confirm spatial and temporal ranges, select models, and adjust hyperparameters. In addition to the operation of training models, it also provides historical models to output identification results, and finally displays the identification results of crop distribution on the Web GIS map.

In fact, agriculture is not the only industry that needs satellite remote sensing applications. AI applications of spatial information have also appeared in various fields as companies in different industries aim to enhance their global competitiveness. For example, surveying and mapping companies that have a large amount of map data can use the AI analysis cloud service platform to store map data while also accelerating the efficiency of digital mapping. Under the severe global climate change and the risk of strong earthquakes, there is a wide variety industrial insurance, agricultural insurance, financial insurance, or disaster insurance are all inseparable from spatial information. The use of remote sensing image recognition to understand insurance targets has long been an international trend.

▲ AI Analysis Cloud Service Architecture for Remote Sensing Big Data

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【解決方案】佐翼科技無人機導入高爾夫球場域 可節省一半人力
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這是一張圖片。 This is a picture.
Using Plant Growth Chambers as an Example - Standardizing Electronic Device Procedures Based on Imaging

In recent years, global climate change and environmental issues have become increasingly severe, causing major impacts on agricultural production Traditional agriculture heavily relies on weather conditions, facing challenges such as unstable crop quality, plummeting yields, and difficult pest control Particularly in Taiwan, agricultural biotech companies and farmers have suffered continuous losses, creating an urgent need for innovative solutions Meanwhile, Taiwan's plant factory industry faces many challenges high equipment and labor costs, an incomplete industrial chain diminishing international competitiveness, and a lack of cooperation among enterprises, all of which limit industry development Additionally, COVID-19the pandemic has highlighted the importance of remote monitoring and management Traditional manual inspections and data collection methods no longer meet the needs of modern agricultural production These issues collectively underline the urgent need for smart 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costs Secondly, the unpredictability brought by climate change has become their nightmare Sudden extreme weather events can destroy their carefully nurtured crops in a short time, causing massive economic losses What's worse, they find themselves increasingly at a disadvantage in international market competition In contrast, large overseas plant factories, with their advanced automation technology and well-organized supply chains, can produce stable-quality agricultural products at lower costs, putting unprecedented pressure on Taiwan's operators On the technical level, they also face numerous challenges Compatibility issues between new and old equipment often put them in a bind, encountering various technical obstacles when trying to integrate different systems Lack of precise data analysis and forecasting capabilities also makes it difficult for them to make production decisions and accurately determine the best growth conditions for each crop Existing monitoring systems provide data that is often disorganized, difficult to interpret and apply Human resource challenges are also severe, with young people generally lacking interest in agricultural work, making it difficult for them to recruit employees with modern agricultural skills Even existing employees often feel exhausted from tedious manual operations and monitoring tasks These problems are intertwined, creating a complex dilemma that leaves plant factory operators confused and anxious They urgently need a comprehensive solution that can enhance factory operational efficiency, reduce costs, and improve product competitiveness, helping them overcome difficulties and regain their footing in the fierce market competition In facing the various challenges of plant factory operators, Taiwan's HaiBoTe company has demonstrated exceptional technical innovation and a flexible customer-oriented development strategy They deeply understand that the solution must be able to seamlessly integrate existing equipment while providing highly intelligent management functions To this end, HaiBoTe's RD team adopted a modular design approach to develop a system that can be flexibly configuredIoTIoT system The core of this system is a smart control hub that can communicate with various sensors and actuators During development, HaiBoTe worked closely with customers, deeply understanding their specific needs and operational environments They even dispatched engineers onsite to observe the daily operations of the plant factories, ensuring that the developed system actually solves practical problems This in-depth cooperation not only helped HaiBoTe optimize their product design but also established a close relationship with customers, laying the foundation for subsequent continuous improvements HaiBoTe's innovation is not just reflected in hardware design but also in their developed intelligent software system This system integrates advanced machine learning algorithms, capable of precise forecasts and optimal 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HaiBoTe's developed smart agriculture solution not only brought revolutionary changes to plant factories but also painted an encouraging picture for the future of the entire agricultural industry The excellence of this system is evident in several aspects firstly, it achieves precise control of the plant growth environment, significantly improving crop yield and quality stability Through advanced artificial intelligence algorithms, the system can forecast and adjust optimum growth conditions based on historical data and real-time monitoring information, ensuring each plant grows in the ideal environment Secondly, it significantly reduces energy consumption and operational costs, improving resource efficiency The intelligent management system optimizes water, electricity, and nutrient supply, reducing waste and lowering manpower costs Additionally, the system's modular design and strong compatibility allow it to seamlessly integrate various new and old equipment, providing a flexible solution for gradual upgrades of plant factories Most importantly, the system injects a sense of technology and modernity into agricultural production, helping to attract the younger generation to the field and injecting new vitality into the industry Looking ahead, HaiBoTe's smart agriculture system has broad application prospects and expansion potential In addition to plant factories, this system can also be applied to traditional greenhouse cultivation, urban agriculture, and even home gardening In the field of aquaculture, similar technology can be used to monitor and optimize the breeding environments for fish or shrimp In the food processing industry, similar intelligent monitoring and forecasting systems can be used to optimize production processes and enhance food safety Even in the pharmaceutical industry, this type of precise environmental management system could be applied to drug research and production processes To further promote this system, HaiBoTe could adopt a 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technology empowering agriculture 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-09」

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