Location and AI — academy.ogc.org

Introduction

AI is helping to automate tasks, improve efficiency, and streamline operations. It is particularly useful for tasks involving large amounts of data and data analysis, and in high-dimensional spaces.

Eg

• analyze traffic patterns and optimize public transport routes.
• monitors crop health, predicts yields
• changes in land use, deforestation, and habitat loss
• to optimize the placement of renewable energy sources
• autonomous boats to collect trash from the seas

AI and Geospatial Data

AI → Machine learning → Deep learning → Generative AI → LLM.

• Supervised learning works with labeled datasets. However, annotating large datasets to train the model is time-consuming and resource-intensive.
• Unsupervised learning identifies patterns, clusters, or structures in the dataset based on the inherent properties of the data.
• Reinforcement learning learns by trial and error and is given feedback for its choices.

Geospatial Applications of AI: Enhancing Analysis Through Images, Sensor Data and 3D Tools

Mapdreamer (Topi Tjukanov): https://tjukanov.org/mapdreamer