Geospatial Data Analyses & Remote Sensing: 5 Classes in 1 Do you need to design a GIS map or satellite-imagery based map for your Remote Sensing or GIS project but you don't know how to do this? Have you heard about Remote Sensing object-based image analysis and machine learning or maybe QGIS or Google Earth Engine but did not know where to start with such analyses? Do you find Remote Sensing and GIS manuals too not practical and looking for a course that takes you by hand, teach you all the concepts, and get you started on a real-life GIS mapping project? I'm very excited that you found my Practical Geospatial Masterclass on Geospatial Data Analyses & Remote Sensing. This course provides and information that is usually delivered in 4 separate Geospatial Data Analyses & Remote Sensing courses, and thus you with learning all the necessary information to start and advance with Geospatial analysis and includes more than 9 hours of video content, plenty of practical analysis, and downloadable materials.

Become proficient in satellite remote sensing, spatial analysis principles, methods, applications, sensors, and GIS! Get this course for only 9.99. Do you find that other remote sensing courses are too short and vague, and do not prepare you for real world problems? Are you looking for a course that goes IN-DEPTH and teaches you all the fundamentals of remote sensing? My course provides a solid foundation to carry out practical, real life remote sensing spatial data analysis and gives you the techniques and knowledge to tackle a variety of geological and environmental problems. This course provides an introduction to remote sensing - the acquisition of information about the earth from a distance, typically via airborne and spaceborne sensors.

Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solvers for these physics to be coupled and differentiable to effectively invert for the subsurface properties of interest. To drastically reduce the computational cost, we introduce a learned coupled inversion framework based on the wave modeling operator, rock property conversion and a proxy fluid-flow simulator. We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fraction of the computational cost. We demonstrate the efficacy of our proposed method by means of a synthetic experiment. Finally, our framework is extended to carbon sequestration forecasting, where we effectively use the surrogate Fourier neural operator to forecast the CO2 plume in the future at near-zero additional cost.

Superconductors have long been considered the principal approach for realizing electronics without resistivity. In the past decade, a new family of quantum materials, "topological materials," has offered an alternative but promising means for achieving electronics without energy dissipation (or loss). Compared to superconductors, topological materials provide a few advantages, such as robustness against disturbances. To attain the dissipationless electronic states, one key route is the so-called "magnetic proximity effect," which occurs when magnetism penetrates slightly into the surface of a topological material. However, observing the proximity effect has been challenging.

Walking back from a business meeting, management consultant Emilia Higashi quickly developed blisters on her feet. She figured there had to be better way to make women's shoes – ones more comfortable and also sustainable, made from plants instead of animal or petroleum products. Soon, she was researching in Italy at the world's biggest footwear trade show. She fell in love with a design called Saccheto, or little sack, that has minimal seams and fits almost like a sock. Then, she looked worldwide for sustainable plant-based materials, including "leathers" made from wine residues, pineapple fibers, cactus and even algae.

SparkCognition, a global leader in artificial intelligence (AI) software solutions perfected for business, is pleased to announce it has signed a definitive agreement to acquire Integration Wizards, a leader in visual AI. Through this acquisition, SparkCognition expands its IP portfolio to include computer vision capabilities, bringing greater value to its industry solutions. The technology leverages new and diverse data sets, including CCTV feeds, drone footage, video from handheld devices, and existing camera infrastructures. The solution can be deployed in hours or days, and helps address critical problems, including safety, security, visual inspection, productivity, and situational awareness. "With advanced visual AI that can recognize complex scenes and activities we further amplify the value we deliver to our customers while leveraging existing infrastructure investments," said Amir Husain, Founder and CEO of SparkCognition.

Scientists used torpedo-shaped robots to map the Arctic seafloor with sonar, revealing massive sinkholes of thawed permafrost. This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. Around 20,000 years ago, the world was so frigid that massive glaciers sucked up enough water to lower sea levels by 400 feet. As the sea pulled back, newly exposed land froze to form permafrost, a mixture of earth and ice that today sprawls across the far north. But as the world warmed into the climate we enjoy today (for the time being), sea levels rose again, submerging the coastal edges of that permafrost, which remain frozen below the water. It's a huge, hidden climate variable that scientists are racing to understand.

It's been years since developers found that Nvidia's main product, the GPU, was useful not just for rendering video games but also for high-performance computing of the kind used in 3D modeling, weather forecasting, or the training of AI models--and it's on enterprise applications such as those that CEO Jensen Huang will focus his attention at the company's GTC 2022 conference this week. Nvidia is hoping to make it easier for CIOs building digital twins and machine learning models to secure enterprise computing, and even to speed the adoption of quantum computing with a range of new hardware and software. Digital twins, numerical models that reflect changes in real-world objects useful in design, manufacturing, and service creation, vary in their level of detail. For some applications, a simple database may suffice to record a product's service history--when it was made, who it shipped to, what modifications have been applied--while others require a full-on 3D model incorporating real-time sensor data that can be used, for example, to provide advanced warning of component failure or of rain. It's at the high end of that range that Nvidia plays.

Abstract. The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute toward the prognostics and health management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis. A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.

A general control policy framework based on deep reinforcement learning (DRL) is introduced for closed-loop decision making in subsurface flow settings. Traditional closed-loop modeling workflows in this context involve the repeated application of data assimilation/history matching and robust optimization steps. Data assimilation can be particularly challenging in cases where both the geological style (scenario) and individual model realizations are uncertain. The closed-loop reservoir management (CLRM) problem is formulated here as a partially observable Markov decision process, with the associated optimization problem solved using a proximal policy optimization algorithm. This provides a control policy that instantaneously maps flow data observed at wells (as are available in practice) to optimal well pressure settings. The policy is represented by a temporal convolution and gated transformer blocks. Training is performed in a preprocessing step with an ensemble of prior geological models, which can be drawn from multiple geological scenarios. Example cases involving the production of oil via water injection, with both 2D and 3D geological models, are presented. The DRL-based methodology is shown to result in an NPV increase of 15% (for the 2D cases) and 33% (3D cases) relative to robust optimization over prior models, and to an average improvement of 4% in NPV relative to traditional CLRM. The solutions from the control policy are found to be comparable to those from deterministic optimization, in which the geological model is assumed to be known, even when multiple geological scenarios are considered. The control policy approach results in a 76% decrease in computational cost relative to traditional CLRM with the algorithms and parameter settings considered in this work.