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June 30, 2025, 10:56 a.m.
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SOURCE: Chivite I. GeoAI. Artificial Intelligence in GIS 2025
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MEDIAINFO
Textbook in PDF format “GeoAI—the integration of spatial analysis, AI, and big data—is creating new insights that promise to transform our understanding of the world.” - Jack Dangermond, president of Esri Discover how organizations are powering greater automation, prediction, and optimization with GeoAI. Organizations across the globe have long relied on geographic information system (GIS) technology to manage and analyze data through the powerful lens of location, helping them tackle some of the toughest business and societal challenges. The emergence of AI-enhanced GIS has opened new opportunities to these groups to automate complex spatial analyses and harness the full breadth of spatial analysis. This democratization of GIS can help everyone make better decisions faster, from city planners and policymakers to businesses, research groups, and constituents. GeoAI: Artificial Intelligence in GIS explores a collection of real-life stories about public- and private-sector organizations as well as NGOs and nonprofits successfully using GeoAI and ArcGIS to manage processes, workflows, policies, and communication. The book also includes a technology overview that provides ideas, strategies, tools, and actions to help jump-start your own use of GeoAI. GeoAI integrates Artificial Intelligence, or AI, with geospatial data, science, and technology to increase understanding and solve spatial problems. AI is the ability of computers to perform tasks that typically require some level of human intelligence and reasoning—through programming that continually adapts, infers patterns, generalizes, and improves output over time. We can use GeoAI for applications such as detecting and categorizing objects in imagery and lidar, identifying clusters and anomalies in data, and making predictions and forecasts. The intersection of GIS, AI, Machine Learning, and Deep Learning creates opportunities that weren’t available before. Machine Learning is a branch of AI in which computers learn patterns within data, and then use what they’ve learned to predict outcomes or make decisions. Machine Learning algorithms are data-driven and operate with minimal human intervention. As machines process more and more data, they are trained so that they automatically “learn” how to adjust their behavior and improve their performance based on previous experience. Deep Learning is available in different formats across the ArcGIS ecosystem, making it increasingly accessible for users of varying skill sets. Whether you’re interested in using ArcGIS Online to test a pretrained model or using the ArcGIS API for Python to create a custom model, there is an ArcGIS option for you. Whichever platform you choose, ArcGIS has the tools to help you accomplish your Deep Learning tasks. Understanding Python unlocks more capabilities in any GIS workflow, and Deep Learning is no exception. The Python API is a Python library for scripting workflows across the ArcGIS suite, including for GIS organization administration, content management, and spatial analysis/data science. Its functionality includes the arcgis.learn module, with various functions for manipulating data and training a Deep Learning model. The Python API is free to install but most of its functionality requires it to be connected to an ArcGIS account. The type of account, user type, and role you need depends on the resource you want to access and the operations you want to undertake. The Python API currently offers flexibility in model training that is unique in the ArcGIS ecosystem. These tools are available only in Python, so with no user interface, you must have working experience in Python to use them successfully. Using the Python API library allows users to leverage some of the best open-source Machine Learning and Deep Learning libraries and seamlessly integrate them into the ArcGIS ecosystem. Another benefit of using the Python API is that it is designed for web-hosted features so you can work with hosted imagery and feature classes Introduction GeoAI Technology Overview Public Sector Applications Private Sector Applications NGO/Nonprofit Applications Next Steps
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