All convolutional layers are followed by LeakyReLU activation. We provide two versions of the image feature mapper. In version "EAD", we use two Table 1 shows a distribution of houses by year and also by region. For both NAIP and Sentinel-2 images, we export from GEE's image pyramid at scale 1, which is The resulting images from both devices have dimensions of around 256 256 . Sentinel-2 data is provided courtesy of "Copernicus Sentinel data 2015-2020" as outlined by the European Space Agency (ESA), and NAIP data is provided courtesy of U.S. Department of Agriculture Guidance for cloud filtering using the Sentinel-2 Cloud Probability dataset is available here.

The SwamCam system monitors your pool and sounds an alarm if someone is around your pool when they shouldn't be--and that's about it. You'll find any number of more flexible alternatives that can achieve the same results for a lot less money, even if they're not ASTM-certified pool alarms. With a name like SwamCam, you might already have an inkling of what this product is designed to do. If you guessed it's designed to keep an eye on your swimming pool, congrats! That said, my expectation is that some of SwamCam's functionality will surprise you--as will its price tag.

Cleaning a swimming pool ranks right up there with scrubbing bathroom floors, but the consequences of neglecting either chore are equally unhealthy. Fortunately, in the age of robots you can ditch the laborious chores of manually skimming leaves from your pool's surface and scrubbing algae from its walls and floors. Invest in a modern robotic pool cleaner and let that machine do that dirty work for you. These labor-saving pool bots come in various shapes and sizes with diverse capabilities, not to mention a wide array of prices. The most sophisticated models go for 2,000 or more.

Remote Sensing Image Captioning (RSIC) presents unique challenges and plays a critical role in applications. Traditional RSIC methods often struggle to produce rich and diverse descriptions. Recently, with advancements in VLMs, efforts have emerged to integrate these models into the remote sensing domain and to introduce descriptive datasets specifically designed to enhance VLM training. This paper proposes RS-MoE, a first Mixture of Expert based VLM specifically customized for remote sensing domain. Unlike traditional MoE models, the core of RS-MoE is the MoE Block, which incorporates a novel Instruction Router and multiple lightweight Large Language Models (LLMs) as expert models. The Instruction Router is designed to generate specific prompts tailored for each corresponding LLM, guiding them to focus on distinct aspects of the RSIC task. This design not only allows each expert LLM to concentrate on a specific subset of the task, thereby enhancing the specificity and accuracy of the generated captions, but also improves the scalability of the model by facilitating parallel processing of sub-tasks. Additionally, we present a two-stage training strategy for tuning our RS-MoE model to prevent performance degradation due to sparsity. We fine-tuned our model on the RSICap dataset using our proposed training strategy. Experimental results on the RSICap dataset, along with evaluations on other traditional datasets where no additional fine-tuning was applied, demonstrate that our model achieves state-of-the-art performance in generating precise and contextually relevant captions. Notably, our RS-MoE-1B variant achieves performance comparable to 13B VLMs, demonstrating the efficiency of our model design. Moreover, our model demonstrates promising generalization capabilities by consistently achieving state-of-the-art performance on the Remote Sensing Visual Question Answering (RSVQA) task.

This article presents an identification benchmark based on data from a public swimming pool in operation. Such a system is both a complex process and easily understandable by all with regard to the stakes. Ultimately, the objective is to reduce the energy bill while maintaining the level of quality of service. This objective is general in scope and is not limited to public swimming pools. This can be done effectively through what is known as economic predictive control. This type of advanced control is based on a process model. It is the aim of this article and the considered benchmark to show that such a dynamic model can be obtained from operating data. For this, operational data is formatted and shared, and model quality indicators are proposed. On this basis, the first identification results illustrate the results obtained by a linear multivariable model on the one hand, and by a neural dynamic model on the other hand. The benchmark calls for other proposals and results from control and data scientists for comparison.

Exploding energy costs have been blamed for the closure of more than 60 public swimming pools across Britain over the past four years. And with the bills for some expected to rise by £100,000 this year, it has left leisure centres scrabbling around for ways to keep the facilities running. It may sound far-fetched, but one leisure centre in Devon is using computer power to heat its swimming pool. The idea works by placing 12 computers inside a white box which is then surrounded by oil to capture the waste heat they produce -- in a similar way to another concept that uses computer servers to heat water in people's homes. Innovative: It may sound far-fetched, but Exmouth Leisure Centre in Devon is using computer power to heat its swimming pool.

Public swimming pools facing closure because of soaring energy bills have been offered a lifeline via new technology to heat the water. Mark Bjornsgaard, the chief executive of the tech startup Deep Green, has trialled the idea in Exmouth, Devon. He has put a small computer data processing centre underneath the pool and the energy from it heats the water. The idea has taken off and up to 20 public pools could be upgraded to the heat system this year. "We built a small data centre in Exmouth leisure centre. Most normal data centres waste the heat that the computers generate. We capture ours and we give it for free to the swimming pool to heat the pool," Bjornsgaard told BBC Radio 4's Today programme.

The field of machine learning and AI is evolving faster than before. New concepts and innovations are developing every day. If you are using machine learning, AI, or data science concepts for your projects or even in your career, keeping up with the latest news in the industry is like one of your everyday activities. In this article, I have shared some interesting articles related to machine learning that I read this week. This intriguing article has revealed an exciting application of AI for collecting unpaid taxes.

The French taxman has now raked in millions of euros from homeowners who failed to report the facilities. New AI software by Google and Capgemini is able to spot pools using aerial imagery which is then cross-checked with land registry databases. During a trial run last year looking at nine French regions the software detected more than 20,000 pools, which led to the collection of some 10 million euros in tax revenue. Since pools boost property values they usually lead to higher property and residency taxes. Private pool sales had already been surging in France before the Covid pandemic, which saw a boom in installations as millions of employees began working from home more often.