Large Language Model (LLM) and it’s Geospatial Applications

Large Language Model (LLM) and it’s Geospatial Applications.

1. For what are you using any form of LLM(chatgpt,gemini,claude)? menti.com Code: 2399 9194

2. Generative AI Classic AI: known output Generative AI: new content creation

3. Large Language Models Billion-parameter models Web-scale pretraining Contextual text generation

4. LLM Tokens Basic units of text (words, parts of words, or characters) Example: "I love cats" → "I", "love", "cats" Embeddings Numerical representations of tokens Enable understanding of relationships between words Context Consideration of surrounding words Determines meaning of ambiguous words Example: "bat" in "baseball bat" vs. "bat flying" Parameters Internal settings adjusted during training More parameters = Better understanding and generation of complex text

5. Attention Mechanism Example Sentence: "Tourist places of Kathmandu" How it Works: Break Down Sentence: "Tourist", "places", "of", "Kathmandu" Assign Weights: High: "Tourist", "places", "Kathmandu" Low: "of" Focus on Keywords : More attention to "Tourist", "places", "Kathmandu" Output: Using the keywords, generate a sentence that makes sense and provides useful information. “Top tourist places in Kathmandu include Swayambhunath, Boudhanath Stupa, and Pashupatinath Temple”

6. State-of-the-Art LLMs Freely Available Openai(gpt3.5, gpt4-o)-chatgpt Google(Gemini)- 2m context window Arthopic(claude) Open Source Meta( LLama) Mistral AI( Mixtral ) Google( Gemma )

7. How I am using LLM daily New way to search anything GitHub Copilot as coding assistance High quality code suggestion Debugging Writing bugless code Writing documentation, and test cases

8. Some Geospatial Use Cases Geocoding Data collection Just ask for data Finding data source Generate osm query ( OSM GPT ) Data Processing Unstructured to Structured Data Data Integration SQL QUERY GIS Analysis

9. Autonomous GPT

10. Let’s make LLM more powerful Fine-Tuning Retrieval-Augmented Generation (RAG) Function Calling

11. Fine Tuning Further training a pre-trained(foundation) model on a specific task or domain. Transfer Learning Domain Adaptation

12. Retrieval Augmented Generation(RAG) Knowledge-Grounded Generation Feed dynamic data as context Vector Database Similarity Search Semantic Search

13. Function Calling

14. AI AGENT

15. DEMO

16. Map Interaction

17. Useful Tools

18. Conclusion Boosting productivity(saving time) More Geospatial foundation model to come More llm powered applications in future