The residents of Potters Bar are working to protect the "green belt" of farms, forests, and meadows that surround London from the endless demand for AI infrastructure. A short drive from London, the town of Potters Bar is separated from the village of South Mimms by 85 acres of rolling farmland segmented by a scribble of hedgerows. In one of the fields, a lone oak serves as a rest stop along a public footpath. Lately, the tree has become a site of protest, too. A poster tied to its trunk reads: "NO TO DATA CENTRE."

Neither President Franklin Delano Roosevelt nor British Prime Minister Winston Churchill, nor any of their senior military or political advisors, saw the Japanese attacks of late 1941 coming. The forces of Imperial Japan achieved total surprise across the Pacific. The intelligence failures in the U.S. leading up to Pearl Harbor were catastrophic. So was Great Britain's general underestimation of the threat from Imperial Japan. The U.K.'s fortress outpost in the Pacific at Singapore was thought to be, if not impregnable, than as close to it as possible.

Sen. Pete Ricketts, R-Neb., spoke with Fox News Digital about his bipartisan bill to codify oversight of foreign adversaries, including China, buying American farmland. EXCLUSIVE: Republican Sen. Pete Ricketts is leading the charge with Democrat Sen. John Fetterman to codify oversight on foreign countries buying American farmland. The bipartisan Agricultural Foreign Investment Disclosure (AFIDA) Improvements Act seeks to implement recommendations published by the Government Accountability Office (GAO) in January 2024, which found the AFIDA was ill-equipped to combat foreign ownership of American agricultural land. "Communist China is our greatest geopolitical threat," Ricketts told Fox News Digital in an exclusive interview, adding, "This is a way for us to improve the disclosure that's going on with regard to the purchase of this agricultural land, so we can take other action if necessary to make sure we're not giving Communist China the opportunity to buy agricultural land." The bill's proposal comes as two Chinese nationals – a University of Michigan post-doctoral research fellow, Yunqing Jian, and Huazhong University of Science and Technology student Chengxuan Han – were held in federal custody after they were accused of smuggling biological materials into the United States.

The traditional deep learning paradigm that solely relies on labeled data has limitations in representing the spatial relationships between farmland elements and the surrounding environment.It struggles to effectively model the dynamic temporal evolution and spatial heterogeneity of farmland. Language,as a structured knowledge carrier,can explicitly express the spatiotemporal characteristics of farmland, such as its shape, distribution,and surrounding environmental information.Therefore,a language-driven learning paradigm can effectively alleviate the challenges posed by the spatiotemporal heterogeneity of farmland.However,in the field of remote sensing imagery of farmland,there is currently no comprehensive benchmark dataset to support this research direction.To fill this gap,we introduced language based descriptions of farmland and developed FarmSeg-VL dataset,the first fine-grained image-text dataset designed for spatiotemporal farmland segmentation.Firstly, this article proposed a semi-automatic annotation method that can accurately assign caption to each image, ensuring high data quality and semantic richness while improving the efficiency of dataset construction.Secondly,the FarmSeg-VL exhibits significant spatiotemporal characteristics.In terms of the temporal dimension,it covers all four seasons.In terms of the spatial dimension,it covers eight typical agricultural regions across China.In addition, in terms of captions,FarmSeg-VL covers rich spatiotemporal characteristics of farmland,including its inherent properties,phenological characteristics, spatial distribution,topographic and geomorphic features,and the distribution of surrounding environments.Finally,we present a performance analysis of VLMs and the deep learning models that rely solely on labels trained on the FarmSeg-VL,demonstrating its potential as a standard benchmark for farmland segmentation.

Finding the cadastral boundaries of farmlands is a crucial concern for land administration. Therefore, using deep learning methods to expedite and simplify the extraction of cadastral boundaries from satellite and unmanned aerial vehicle (UAV) images is critical. In this paper, we employ transfer learning to train a U-Net model with a ResNet34 backbone to detect cadastral boundaries through three-class semantic segmentation: "boundary", "field", and "background". We evaluate the performance on two satellite images from farmlands in Iran using "precision", "recall", and "F-score", achieving high values of 88%, 75%, and 81%, respectively, which indicate promising results.

Collaboration is a cornerstone of society. In the real world, human teammates make use of multi-sensory data to tackle challenging tasks in ever-changing environments. It is essential for embodied agents collaborating in visually-rich environments replete with dynamic interactions to understand multi-modal observations and task specifications. To evaluate the performance of generalizable multi-modal collaborative agents, we present TeamCraft, a multi-modal multi-agent benchmark built on top of the open-world video game Minecraft. The benchmark features 55,000 task variants specified by multi-modal prompts, procedurally-generated expert demonstrations for imitation learning, and carefully designed protocols to evaluate model generalization capabilities. We also perform extensive analyses to better understand the limitations and strengths of existing approaches. Our results indicate that existing models continue to face significant challenges in generalizing to novel goals, scenes, and unseen numbers of agents. These findings underscore the need for further research in this area. The TeamCraft platform and dataset are publicly available at https://github.com/teamcraft-bench/teamcraft.

Land Use Land Cover (LULC) analysis on satellite images using deep learning-based methods is significantly helpful in understanding the geography, socio-economic conditions, poverty levels, and urban sprawl in developing countries. Recent works involve segmentation with LULC classes such as farmland, built-up areas, forests, meadows, water bodies, etc. Training deep learning methods on satellite images requires large sets of images annotated with LULC classes. However, annotated data for developing countries are scarce due to a lack of funding, absence of dedicated residential/industrial/economic zones, a large population, and diverse building materials. BD-SAT provides a high-resolution dataset that includes pixel-by-pixel LULC annotations for Dhaka metropolitan city and surrounding rural/urban areas. Using a strict and standardized procedure, the ground truth is created using Bing satellite imagery with a ground spatial distance of 2.22 meters per pixel. A three-stage, well-defined annotation process has been followed with support from GIS experts to ensure the reliability of the annotations. We performed several experiments to establish benchmark results. The results show that the annotated BD-SAT is sufficient to train large deep learning models with adequate accuracy for five major LULC classes: forest, farmland, built-up areas, water bodies, and meadows.

Customers shopping for a used car can sometimes feel overwhelmed digging through countless specs and reviews, but CarMax, the largest used car retailer in the U.S., is making it easier for customers to find the most useful information. Thanks to powerful AI language models, potential buyers can now see summaries of customer reviews for every make, model and year of vehicle that CarMax sells, about 5,000 combinations in a vast inventory of approximately 45,000 cars. The summaries provide easy-to-read takeaways from real customer reviews: whether it's a great family car, how comfortable the ride is or if there's enough space to pack for weekend adventures. CarMax has also used the models to create new website content that allows customers to easily see what's new for each version of a car, helping them decide whether new features are worth splurging on. CarMax generated the massive amount of original content in just a few months -- a rate previously impossible -- with powerful GPT-3 natural language models built by the company OpenAI.

We present two large datasets of labelled plant-images that are suited towards the training of machine learning and computer vision models. The first dataset encompasses as the day of writing over 1.2 million images of indoor-grown crops and weeds common to the Canadian Prairies and many US states. The second dataset consists of over 540,000 images of plants imaged in farmland. All indoor plant images are labelled by species and we provide rich etadata on the level of individual images. This comprehensive database allows to filter the datasets under user-defined specifications such as for example the crop-type or the age of the plant. Furthermore, the indoor dataset contains images of plants taken from a wide variety of angles, including profile shots, top-down shots, and angled perspectives. The images taken from plants in fields are all from a top-down perspective and contain usually multiple plants per image. For these images metadata is also available. In this paper we describe both datasets' characteristics with respect to plant variety, plant age, and number of images. We further introduce an open-access sample of the indoor-dataset that contains 1,000 images of each species covered in our dataset. These, in total 14,000 images, had been selected, such that they form a representative sample with respect to plant age and ndividual plants per species. This sample serves as a quick entry point for new users to the dataset, allowing them to explore the data on a small scale and find the parameters of data most useful for their application without having to deal with hundreds of thousands of individual images.

Organic farming, which does not involve any agricultural chemicals or synthesized fertilizers, is attracting attention in Japan, especially because it puts less strain on the environment. While the government has launched a strategy to promote organic agriculture, there are still many challenges to overcome, including ways to reduce physical burdens on farmers and costs, and expand sales channels. "We've recently been seeing an increase in the number of environmentally aware young customers," said Naoya Okada, president of Bio c' Bon Japon Co., which operates an organic food store in Tokyo's Ebisu district. Organic farming uses compost for soil cultivation, with farmers digging up weeds and not relying on agricultural chemicals. Momentum for compiling international organic agriculture standards is building, especially in Europe, as the farming method is believe to contribute to the conservation of biodiversity and the fight against global warming.