Spatial Data Science Events, Videos, Webinars, and Courses

It's a challenge to bring together data that is prepped and ready to use. If you want to make your data ready for analysis or automate data aggregation to hydrate a dashboard, ArcGIS Notebooks can help. Watch this session on leveraging this technology to automate, prepare your data, and aggregate for a variety of workflows. Topics covered in this session include a brief overview of ArcGIS Notebooks, leveraging the automation capabilities of Python for spatial content management and analysis workflows, setting up and scheduling tasks to run within your portal at prescribed dates/times as well as a deep dive into the parameterization capabilities of ArcGIS Notebooks

Explore how to apply the imagery deep learning capabilities of ArcGIS to automate map production, assess damaged structures post-calamity, count trees for agriculture census, monitor land cover-change, and count features from drone imagery and full motion video. Topics covered include an introduction to artificial intelligence, machine learning, and deep learning, overview of ArcGIS Notebooks and the arcgis.learn module within ArcGIS API for Python, overview of the ArcGIS Enterprise deep learning system architecture and more. 

Digital twins, while important for efficient transportation, urban planning and design, can be expensive to create and maintain. Learn how the capabilities of ArcGIS enable the automation of high-fidelity object extraction from lidar point clouds and allow faster and more efficient content acquisition. Additionally, this webinar will include coverage of topics such as ArcGIS capabilities for lidar processing, preparing training data for model training, training models using arcgis.learn PointCNN, model training using PointNet, post-processing using the machine learning capabilities of ArcGIS and more. 

Almost 80 percent of the world's data is unstructured, such as data found in emails, speech transcripts, briefings, social media posts, news reports, and more. Much of this unstructured data has a location variable tied to it (text origin or locations described within) and can be integrated into spatial analysis workflows such as analyzing police reports to understand a changing crime landscape or mining news articles to understand disease spread. Topics covered in this session include an introduction to natural language processing (NLP), labeling and preparing training data, extracting entities using the arcgis.learn EntityRecognizer module, integration with third-party NLP frameworks, demos and industry-specific use cases and more. 

ArcGIS enables data preparation for modeling and is host to predictive analytical tools tuned to handle spatial data. In this webinar, you will learn how to use ArcGIS to model and explore spatial data to uncover relationships and predict spatial outcomes. The application of generalized linear regression (GLR), geographically weighted regression (GWR), and Forest-based Classification and Regression will be shown. Also, you will learn how to use geoprocessing tools for feature engineering and data preparation for predictive modeling and how ArcGIS can integrate with machine learning frameworks.