Esri's continued advancements in data storage and parallel and distributed computing make solving problems at the intersection of machine learning (ML) and GIS increasingly possible. ML refers to a set of data-driven algorithms and techniques that automate the prediction, classification, and clustering of data. ML can be computationally intensive and often involves large and complex data. It can play a critical role in spatial problem-solving in a wide range of application areas from multivariate prediction to image classification to spatial pattern detection. Based on the analysis of seven years of traffic accident data, the model predicted areas with the highest risk for accidents.
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.
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.
ArcGIS Pro is the primary desktop client for image visualization, processing and management. The Image Analyst extension extends ArcGIS Pro to be an image analysis workstation, with tools for improved image interpretation, feature extraction and imagery analysis. The extension is designed for imagery analysts, geospatial analysts, and image scientists who focus on image interpretation and the creation of information products from imagery. As part of the ArcGIS Pro 2.3 release, the Image Analyst extension has been expanded with several advanced capabilities and features. In addition to enhancements in deep learning, motion video, and image analysis functions, there has been a strong focus on quality improvements and bug fixes in the stereo and image space capabilities.
ArcGIS is an industry standard for geospatial development and management. A geodatabase is the database used to store and manage the spatial data. Learning ArcGIS Geodatabases offers a comprehensive working and practical experience for readers who are interested in knowing about ArcGIS. Then, the book focuses extensively on modeling and optimizing geodatabases. Finally, you will be able to work comfortably with datasets, annotations, and relationship classes, making it easier to migrate from a legacy database to an ArcGIS geodatabase.