Location and AI — academy.ogc.org
From wikiluntti
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.
Mapdreamer (Topi Tjukanov): https://tjukanov.org/mapdreamer
Generative AI can create new content. It works by studying large sets of data in order to understand patterns.
- Can generate fake but realistic satellite images, point clouds, or other datasets.
- GeoGPT
- Simulating scenarios (eg urban growth, flooding, or evacuation routes).
