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 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 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

Technology also carries the aroma of coffee Situated on Gaogong Road in the Southern District of Taichung, the original Black Wo Coffee store covers a space of 28 ping, filled with the scent of coffee mixed with cultural creativity and technology Since its establishment in October 2016, Black Wo Coffee has expanded to 7 directly managed stores and 28 franchise stores across Taiwan Among the 15,000 coffee sellers nationwide, Black Wo Coffee has risen uniquely through the use of AI technology to create an unmanned intelligent vending machine that brews exquisite and aromatic coffee in just one minute Black Wo Coffee's physical store creates a culturally creative and fashionable atmosphere Image Black Wo Coffee official website According to the International Coffee Organization ICO, Taiwanese people consume 285 billion cups of coffee annually, with the market size exceeding 70 billion NT dollars Ambitiously, as per Starbucks' survey, by 2018 the overall Taiwanese coffee market reached 72 billion NT dollars and rose to 90 billion by 2020 Over the last five years, the Taiwanese coffee market has expanded annually by about 20, showing remarkable growth potential Coffee demand presents incredible business opportunities, growing at a rate of 20 annually With coffee now being a symbol of fashionable consumption in Taiwan, aside from first-tier coffee shops like Starbucks and Louisa, there are convenience stores like 7-11 and FamilyMart, and numerous boutique coffee houses scattered through the streets and alleys How to capture consumer attention and stand out in the 'red ocean' of the coffee market requires flexibility and creativity, understanding consumer needs and tastes, which are also essential for cultivating brand loyalty Beyond physical storefronts, Black Wo Coffee is also actively developing digital channels Its ecommerce platform includes the official website, PChome, momo, and group-buying hosts, providing multiple channels and ensuring steady growth in performance Even so, the founder of Black Wo Coffee, Lin Pei-ni, continually seeks innovation Due to the passive and scattered situation with franchise stores in the first three years, it was difficult to actively grasp market trends and the company noticed a certain lag in communicating with consumers and keeping up with brand dynamics, making it challenging to cultivate loyal brand advocates Artisan boutique coffee is deeply beloved by consumers Image Black Wo Coffee official website Through the AI Eagle Eye System, market intelligence costs are significantly reduced To address the dual challenges of not being able to quickly capture market trends and high market research costs, Black Wo Coffee introduced the AI Eagle Eye System in 2020 to scout market intelligence By comprehensively crawling articles from social websites, news platforms, and forums, automatically tagging, and suitably filtering, this system scanned 4,858 articles using 24,290 keywords, enabling precise insights into consumer preferences at minimal costs At the same time, after launching new products, not only can franchise stores be notified promptly, but the acceptance level of consumers can also be assessed through social platforms It serves as a reference for whether to promote aggressively Through the collection of data and analysis by AI algorithms, consumer-preferred flavors are selected, reducing the risks associated with new launches and increasing the success rate of new products Therefore, in 2021, Black Wo Coffee boldly explored new markets by introducing the world's first AIoT smart coffee innovative concept in collaboration with Pxmart for the first 'Intelligent Supermarket', integrating Black Wo Coffee to create an unmanned intelligent hand-drip coffee machine for consumers to enjoy a unique flavor experience Insight into consumer tastes leads to the creation of AIoT Unmanned Intelligent Vending Machines Taiwan's first Pxmart 'Intelligent Supermarket' in Neihu, Taipei introduces the world's first AIoT smart coffee concept store, able to interact with the AI smart coffee vending machine, AI hand-washing coffee machine, and AI vacuum cold brew machine through a mobile app, meeting three different coffee technology experiences in one place The self-service area features the only unmanned intelligent coffee vending machine in Taiwan that uses chilled milk to make milk foam, selecting Black Wo's 5A grade milk, and completing the payment, grinding, and brewing all within one minute The first Pxmart 'Intelligent Supermarket' was established on Ruiguang Road in Neihu District, Taipei Image Pxmart FB fan page The Pxmart Intelligent Supermarket features an AI smart coffee vending machine, which is operated using an app to enjoy aromatic coffee Image Pxmart FB fan page Now, with the addition of AI technology elements, drinking coffee is not just about having coffee it also brings more brand-new tech experiences and conveniences to consumers「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」