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Damage assessment using deep learning in ArcGIS

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In the aftermath of a natural disaster, response and recovery efforts can be drastically slowed down by manual data collection. Traditionally, insurance assessors and government officials have to rely on human interpretation of imagery and site visits to assess damage and loss. But depending on the scope of a disaster, this necessary process could delay relief to disaster victims. Article Snapshot: At this year's Esri User Conference plenary session, the United Services Automobile Association (USAA) demonstrated the use of deep learning capabilities in ArcGIS to perform automated damage assessment of homes after the devastating Woolsey fire. This work was a collaborative prototype between Esri and USAA to show the art of the possible in doing this type of damage assessment using the ArcGIS platform.


Deep learning models in arcgis.learn

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Artificial Intelligence (AI) has arrived. It is not science fiction anymore. Computers already recognize objects in images and understand speech and language at least as well as, if not better than, humans. This has been made possible with rapid advances in hardware, vast amounts of training data, and innovations in machine learning algorithms such as deep neural networks. Deep learning is the driving force behind the current AI revolution and is giving intelligence to today's self-driving cars, smartphone and smart speakers, and making deep inroads into radiology and even gaming.


Microsoft and Esri launch Geospatial AI on Azure

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Integrating geography and location information with AI brings a powerful new dimension to understanding the world around us. This has a wide range of applications in a variety of segments, including commercial, governmental, academic or not-for-profit. Geospatial AI provides robust tools for gathering, managing, analyzing and predicting from geographic and location-based data, and powerful visualization that can enable unique insights into the significance of such data. Available today, Microsoft and Esri will be offering the GeoAI Data Science Virtual Machine (DSVM) as part of our Data Science Virtual Machine/Deep Learning Virtual Machine family of products on Azure. This is a result of a collaboration between the two companies and will bring AI, cloud technology and infrastructure, geospatial analytics and visualization together to help create more powerful and intelligent applications.


2018 Esri Partner Conference and Developer Summit – Part 2

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The timing worked superbly, like the best Swiss clockwork: A few days before winter made a comeback in Switzerland, I sat in a plane to Los Angeles. Nevermind that California also had slightly cooler temperatures than usual – it was definitely preferable over the polar cold air masses that firmly occupied Switzerland. Even the place names felt evocative: Santa Cruz, Big Sur, and San Francisco. For two weeks I would cruise California, before making my way back to L.A. and then Palm Springs in order to attend the 2018 Esri Partner Conference and Developer Summit together with my colleague, Nicole Sulzberger. In what follows, we describe what we learned during the two Esri events: the latest news about developments at Esri.


Where Deep Learning Meets GIS

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The field of artificial intelligence (AI) has progressed rapidly in recent years, matching or, in some cases, even surpassing human accuracy at tasks such as image recognition, reading comprehension, and translating text. The intersection of AI and GIS is creating massive opportunities that weren't possible before. AI, machine learning, and deep learning are helping us make the world better by helping, for example, to increase crop yield through precision agriculture, fight crime by deploying predictive policing models, and predict when the next big storm will hit and being better equipped to handle it. Broadly speaking, AI is the ability of computers to perform a task that typically requires some level of human intelligence. Machine learning is one type of engine that makes this possible.