Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.

Syntiant, an Irvine, California-based startup with big name backers like Intel and Microsoft, said its custom chips could be used to push Amazon's Alexa into smaller, battery-powered devices like wearables and wireless headphones that wake themselves up when they hear the voice assistant's wake word or other commands. Amazon just approved its deep learning accelerators for use with Alexa Voice Services (AVS). The company's NDP100 can be programmed to continuously listen for 64 wake words or specific sounds--like glass breaking or a baby crying--with power consumption in the range of 150 uW and more than 100 KB of SRAM. "These chips are purpose-built for keyword spotting such as wake words like Alexa, and now our processors can be used for quickly developing voice applications in battery-powered devices," chief executive Kurt Busch said in a statement. Syntiant, which was founded by former engineering executives from Broadcom, has raised over $30 million in funding from investors including Microsoft's M12, Amazon's Alexa Fund, Applied Ventures, Intel Capital, Motorola Ventures, and Robert Bosch Venture Capital.

Attempts from different disciplines to provide a fundamental understanding of deep learning have advanced rapidly in recent years, yet a unified framework remains relatively limited. In this article, we provide one possible way to align existing branches of deep learning theory through the lens of dynamical system and optimal control. By viewing deep neural networks as discrete-time nonlinear dynamical systems, we can analyze how information propagates through layers using mean field theory. When optimization algorithms are further recast as controllers, the ultimate goal of training processes can be formulated as an optimal control problem. In addition, we can reveal convergence and generalization properties by studying the stochastic dynamics of optimization algorithms. This viewpoint features a wide range of theoretical study from information bottleneck to statistical physics. It also provides a principled way for hyper-parameter tuning when optimal control theory is introduced. Our framework fits nicely with supervised learning and can be extended to other learning problems, such as Bayesian learning, adversarial training, and specific forms of meta learning, without efforts. The review aims to shed lights on the importance of dynamics and optimal control when developing deep learning theory.

Remote Sensing Images from satellites have been used in various domains for detecting and understanding structures on the ground surface. In this work, satellite images were used for localizing parking spaces and vehicles in parking lots for a given parcel using an RCNN based Neural Network Architectures. Parcel shapefiles and raster images from USGS image archive were used for developing images for both training and testing. Feature Pyramid based Mask RCNN yields average class accuracy of 97.56% for both parking spaces and vehicles

IBM designed Summit, the fastest supercomputer on Earth, to run the calculation-intensive models that power modern artificial intelligence (AI). Now MIT is about to get a slice. IBM pledged earlier this year to donate an $11.6 million computer cluster to MIT modeled after the architecture of Summit, the supercomputer it built at Oak Ridge National Laboratory for the U.S. Department of Energy. The donated cluster is expected to come online this fall when the MIT Stephen A. Schwarzman College of Computing opens its doors, allowing researchers to run more elaborate AI models to tackle a range of problems, from developing a better hearing aid to designing a longer-lived lithium-ion battery. "We're excited to see a range of AI projects at MIT get a computing boost, and we can't wait to see what magic awaits," says John E. Kelly III, executive vice president of IBM, who announced the gift in February at MIT's launch celebration of the MIT Schwarzman College of Computing.

IBM designed Summit, the fastest supercomputer on Earth, to run the calculation-intensive models that power modern artificial intelligence (AI). Now MIT is about to get a slice. IBM pledged earlier this year to donate an $11.6 million computer cluster to MIT modeled after the architecture of Summit, the supercomputer it built at Oak Ridge National Laboratory for the U.S. Department of Energy. The donated cluster is expected to come online this fall when the MIT Stephen A. Schwarzman College of Computing opens its doors, allowing researchers to run more elaborate AI models to tackle a range of problems, from developing a better hearing aid to designing a longer-lived lithium-ion battery. "We're excited to see a range of AI projects at MIT get a computing boost, and we can't wait to see what magic awaits," says John E. Kelly III, executive vice president of IBM, who announced the gift in February at MIT's launch celebration of the MIT Schwarzman College of Computing.

Planning for multi-robot coverage seeks to determine collision-free paths for a fleet of robots, enabling them to collectively observe points of interest in an environment. Persistent coverage is a variant of traditional coverage where coverage-levels in the environment decay over time. Thus, robots have to continuously revisit parts of the environment to maintain a desired coverage-level. Facilitating this in the real world demands we tackle numerous subproblems. While there exist standard solutions to these subproblems, there is no complete framework that addresses all of their individual challenges as a whole in a practical setting. We adapt and combine these solutions to present a planning framework for persistent coverage with multiple unmanned aerial vehicles (UAVs). Specifically, we run a continuous loop of goal assignment and globally deconflicting, kinodynamic path planning for multiple UAVs. We evaluate our framework in simulation as well as the real world. In particular, we demonstrate that (i) our framework exhibits graceful coverage given sufficient resources, we maintain persistent coverage; if resources are insufficient (e.g., having too few UAVs for a given size of the enviornment), coverage-levels decay slowly and (ii) planning with global deconfliction in our framework incurs a negligibly higher price compared to other weaker, more local collision-checking schemes. (Video: https://youtu.be/aqDs6Wymp5Q)

Proactively perceiving others' intentions is a crucial skill to effectively interact in unstructured, dynamic and novel environments. This work proposes a first step towards embedding this skill in support robots for search and rescue missions. Predicting the responders' intentions, indeed, will enable exploration approaches which will identify and prioritise areas that are more relevant for the responder and, thus, for the task, leading to the development of safer, more robust and efficient joint exploration strategies. More specifically, this paper presents an active intention recognition paradigm to perceive, even under sensory constraints, not only the target's position but also the first responder's movements, which can provide information on his/her intentions (e.g. reaching the position where he/she expects the target to be). This mechanism is implemented by employing an extension of Monte-Carlo-based planning techniques for partially observable environments, where the reward function is augmented with an entropy reduction bonus. We test in simulation several configurations of reward augmentation, both information theoretic and not, as well as belief state approximations and obtain substantial improvements over the basic approach.

Esri Inc., Redlands, CA 92 373 Summary Abstract: Artificial Intelligence ( AI) ha s received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society . This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches . We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applicatio ns and p ossible future directions. Definitions 1. Artificial Intelligence: The study and design of machines or computational methods that can perform tasks that normally require human intelligence. Description/body 1. AI and Geography Artificial Intelligence (AI) has received tremendous attention in recent years from academia, industry, and the general public. Despite its recent popularity, the field was born back in 1956 at a workshop at Dartmouth College (McCarthy 1956) .

Formerly Statoil, we are 20,000 committed colleagues developing oil, gas, wind and solar energy in more than 30 countries worldwide. Driven by our Nordic urge to explore beyond the horizon, and our dedication to safety, equality and sustainability, we're building a global business on our values and the energy needs of the future. The role of the COO organisation is to drive consistent long term safe and efficient operational performance and value creation. The COO organisation is responsible for the corporate improvement programs and works closely with the line in continuously improving Equinor's performance. Are you interested in building great machine learning products that can help Equinor transform into a greener and more competitive energy company?