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

Have you had your morning coffee yet Over the past decade, Taiwan has gradually formed a coffee drinking culture With the advancement of AI technology, autonomous coffee roasters can now rely on AI for precise location setting while also cultivating a loyal customer base Let's see how this is done According to the International Coffee Organization ICO, Taiwanese consume approximately 285 billion cups of coffee annually, with the coffee market in Taiwan estimated at 80 billion TWD, growing about 20 each year In recent years, the 'drinking coffee' culture in Taiwan has become synonymous with popularity, with coffee being the most frequently chosen daily beverage by 65 of the population Coffee enthusiasts, particularly the more avid ones, are willing to pay more for coffee beans that suit their tastes An increasing number of unmanned drink kiosks have also begun to appear in the Taiwanese beverage market Unmanned coffee beverage shops face difficulties in expanding quickly, primarily due to two major issues one is the appropriateness of customer flow and machine placement locations which still rely on manual analysis the second is penetrating the market of mid to high-end coffee lovers accurately AI resolves two major challenges for autonomous coffee roasters suitable placement and cultivating a loyal customer base To tackle these issues and help autonomous coffee roasters quickly break into the market, Raysharp Electronics intends to implement AI for people flow counting analysis and unfamiliar face recognition These technologies aim to calculate the crowd size at potential roaster locations and classify consumers by gender and age for more precise market analysis They also provide multiple choices for the roasting of raw coffee beans, offering a more customized service tailored to the needs and tastes of professional coffee aficionados with a pack of 'high-quality roasted beans' Since 2018, the rise of unmanned stores has been mainly due to owners wanting to reduce persistently rising rent and personnel costs However, the initial assessment of store locations still requires hourly labor expenses for manual estimation of customer flow, leading to possible miscalculations of both on-site consumers and passerby traffic These inaccuracies may prevent precise real-time analysis of customer flow, or even misguided estimations of operational efficacy after a trial run, thus missing the optimal timing for loss-preventing location retraction Raysharp Electronics introduces autonomous coffee roasters equipped with AI-based people counting analysis and facial recognition Raysharp Electronics combines AI people counting analysis and facial recognition with the coffee trend known as 'black gold', addressing the preferences of numerous coffee connoisseurs in Taiwan who enjoy personally selecting coffee beans at bulk stores and frequenting high-quality grinding cafes or chain coffee shops A new concept for the first autonomous coffee roaster offering choices based on the origin, variety, and roasting methods of coffee beans has emerged AI coffee roasters enhance customer loyalty and materials management efficiency by 20 For the advanced development of autonomous coffee roasters, Raysharp Electronics engineers have equipped the AI NVIDIA development platform on the basis of TCNNFacenet Through AI, tens of thousands of images related to gender and age are used for sample training, allowing even first-time coffee roasting customers to be easily classified using unfamiliar face recognition This gains consumer trust, enhances willingness to use, and allows for recording purchase information and future product recommendations, leading to consumer purchase behavior analysis This information helps owners tailor future material preparation based on consumer preferences for different coffee beans, reducing raw material transportation and storage issues, and improving material management efficiency by 20 Moreover, by placing these autonomous coffee roasters in high-traffic areas, owners can use cameras to capture the crowd and assess whether the machine location has an adequate customer base, quickly analyzing whether to reposition the machines, and more easily targeting the best locations for middle and high-end coffee lovers The unmanned coffee roaster features a professional roasting mode interface, providing options based on the origin and variety of coffee beans, their roasting methods light, medium, deep, and related temperature, wind speed, and timing settings If improvement needs arise during the process, engineers can adjust firmware parameters and also assist in integration with the owner's ordering system Staff members briefly describe the operation of the autonomous coffee roaster 'Black Gold' penetrates deeper into coffee shops, science parks, and commercial buildings through AI This autonomous coffee roaster targets coffee connoisseurs and can be placed in middle to high-end coffee shops to roast more customized coffee beans than those available in bulk stores Upon completing a batch of coffee beans, it immediately provides them to professional technicians within the coffee shops for grinding and manual brewing The remaining roasted beans can also be taken home for brewing and enjoyment It also adds value to coffee shops by better understanding consumer preferences for coffee beans and launching more customer-attracting drink promotions and appropriate inventory management In addition to coffee shops, the autonomous coffee roaster can also utilize AI-based people counting analysis to precisely set up near scientific parks and commercial buildings, offering high-quality coffee beans for office brewing to internal employees with high coffee consumption needs Furthermore, implementing a physical membership system can initiate coffee bean purchase promotions or periodic payment incentives, thus attracting new clients and cultivating existing customer loyalty and retention The operation interface of the smart autonomous coffee roaster「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

More Preparation Less Loss The Taiwan Food Bank Association, a non-profit organization, collects donations daily from wholesalers, retailers, manufacturers, and even kind-hearted individuals across Taiwan They also rescue consumable materials that are about to be discarded, properly allocate and deliver to households in need, aiding local underprivileged populations When natural disasters such as earthquakes, landslides, mudslides, typhoons, floods, and droughts occur in Taiwan, the food bank's resources can be immediately deployed for disaster relief This field verification unit is the Nantou County Red Cross AssociationOne of the food bank locations, hereinafter referred to as the Nantou Red CrossIs responsible for tasks like pre-disaster supplies preparation and disaster relief material distribution, helping the government bear the responsibility of disaster relief and aid In Taiwan, various natural disasters have characteristics of different duration and spatial coverage, wide or narrow With the normalization of extreme weather, the scale and number of disasters are gradually increasing and becoming harder to predict The required amount and type of materials differ by disaster, and they must address the lifestyles of the affected areas, rescue needs, traffic conditions, geographical restrictions, and other factors for varied material allocation, facing numerous challenges Typhoon Kanu severely damaged transportation in Nantou mountain areas Nantou County Red Cross planned the mountainous route Puli gt Fazhi Elementary School gt Qin'ai Village gt Aowanda to deliver supplies Disasters happen repeatedly We need to be prepared at all times Effective disaster preparedness can mitigate the impact, including swift response to material needs in affected areas, aid distribution, and even psychological support, providing added security for life and property of those in disaster zones Lack of Timeliness in Disaster Information To improve the living conditions and address the lack of supplies in remote areas, the Taiwan Food Bank Association has partnered with the Nantou Red Cross and has successively established food bank points in Nantou City, Puli, and Ren'aiLixing, Ruiyan, XinyiWangmei, Tongfu, Shuili, Lugu and Caotun among others9establish food bank locations, providing supplies worth a certain amount per household every month6001000in New Taiwan Dollars However, many challenges still need to be overcome during natural disasters For example, when typhoons, earthquakes, and landslides occur, the information source for disaster relief dispatch systems relies on post-disaster reports The time lag between reporting, response, and execution prevents timely adjustment and distribution of 'disaster relief' supplies based on the needs of affected areas, affecting rescue efficiency due to lack of timely information The 'preparedness' supplies of the Nantou Red Crosssuch as dry food, water, instant noodles, etc,are recorded manually in terms of stock, expiration dates, and distribution,When a disaster occurs, there is a chance that 'preparedness' supplies have expired and cannot become 'disaster relief' supplies It’s also possible that both conditions mentioned above occur simultaneously, leading to a need for more time to reassign 'preparedness' supplies into usable 'disaster relief' materials On the other hand, upon receiving information about shortages in disaster areas, the supplies donated by the public often grossly differ from the actual needs of the disaster zone, leading to an excess of supplies The Process of Material Operations Before and After a Natural Disaster AIAnticipating Natural Disasters Reinforcing the Accuracy of Preparedness Material Dispatch Application API Technology connects to compute the state of the climate, the intensity of disaster rescues, prioritizing the main tasks of the Nantou Red Cross and the needed areas of search and rescue Coordinated with the existing heavy rain and typhoon simulation disaster training of the Nantou Red Cross, a 'Natural Disaster Emergency Preparedness Material Dispatch and Supplement Decision System' is establishedreferred to as the Emergency Preparedness Material System。 In material management, inventory data along with immediate supply data are entered into the Emergency Preparedness Material System for comparison and analysis, helping the Nantou Red Cross quickly recognize materials like cookiesdry food, beverages, frozen food, toilet paper, etc, and determining whether they should be 'preparedness' materials or regularly distributed materials Adding to this, information forecasting understands the potential disaster conditions in remote areas, facilitating food delivery, addressing both front-end food wastage and backend practical needs When a natural disaster occurs, it enables faster response and decision-making, completing material deployment, hence increasing the speed of material operation transition20。 AI Emergency Preparedness Material System Helps Rapidly Adapt Material Distribution Through the field verification of the Nantou Red CrossAIthe system, material management, and related applications are promoted to more emergency response organizations in different areas, while continuously improving the alert functions within the Emergency Preparedness Material System, strengthening the technological foundation for alerts, enhancing prediction accuracySystem immediacy, and optimizing the data collection and analysis process Simultaneously, it can collaborate with government agencies, meteorological departments, or other rescue teams to discuss integrating more data sources, establishing a mechanism to share resources and data promptly, sharing information in real-time, helping more emergency response organizations enhance their disaster response abilities, seizing the golden rescue time 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-12-12」