South Florida GIS Explo 2023. West Palm Beach, F

Guan, Boyuan

Juhasz, Levente

English

DigitalCommons@Florida International University

Florida International University FIU Digital Commons GIS Center GIS Center 8-24-2023 Utilizing Large Language Models in geographic contexts - Experiences from the FIU GIS Center Levente Juhasz Boyuan Guan Follow this and additional works at: https://digitalcommons.fiu.edu/gis This work is brought to you for free and open access by the GIS Center at FIU Digital Commons. It has been accepted for inclusion in GIS Center by an authorized administrator of FIU Digital Commons. For more information, please contact dcc@fiu.edu. Utilizing Large Language Models in geographic contextsExperiences from the FIU GIS CenterSouth Florida GIS ExpoWest Palm Beach, FLAugust 24, 2023Levente Juhasz & Boyuan (Keven) GuanOutline• Introduction• Case Study #1: Towards Understanding the Spatial Literacy of ChatGPT• Case Study #2: Mapping with ChatGPT• Case Study #3: ChatGPT as a Mapping Assistant• Case Study #4: GeoGPTOutline• Introduction• Case Study #1: Towards Understanding the Spatial Literacy of ChatGPT• Case Study #2: Mapping with ChatGPT• Case Study #3: ChatGPT as a Mapping Assistant• Case Study #4: GeoGPTGIS Center at FIU• Supports geospatial teaching and research within FIU• Operates computer labs on 2 campuses• Provides software, data and service licenses to the FIU community• Conducts original research• Works with local, state and federal partners• 4 faculty• 4 full-time staff• Part-time student and OPS• Some of our partners:GeoAI TeamLevente JuhászResearch Asst. ProfessorInterim DirectorGeomatics/GIScienceBoyuan Keven GuanLead Digital Project DeveloperComputer ScienceJinwen XuResearch AssociateGeography/GIScienceWencong CuiGeospatial Developer IIComputer Science/GeographyLarge Language ModelsOpenAI ChatGPT 3.5Google BardLarge Language ModelsWhat are they trained on? How do they work?The location of the South FL GIS Expo is ...West cool!PalmtheConvention... ...InputPredictionLarge Language Models• Learning of geographic concepts• What is a location? What is a place?• At what distance does something become "nearby"?• Where is my school?• Human learning these concepts and skills is fundamentally different from ingesting large amounts of texts.Outline• Introduction• Case Study #1: Towards Understanding the Spatial Literacy of ChatGPT• Case Study #2: Mapping with ChatGPT• Case Study #3: ChatGPT as a Mapping Assistant• Case Study #4: GeoGPTTowards understanding the spatial literacyof ChatGPT• Motivation• AI is here and is not going anywhere• How good are LLMs in grasping spatial concepts today?• What does it all mean for teaching GIS skills in a higher education?• Goals• Configure ChatGPT to take a real introductory GIS exam• Quantitatively assess ChatGPT's performance• Outcomes• Gain insights into the potential applications and challenges of LLMs in spatially-oriented fields• further insights into the strengths and weaknesses of ChatGPT’s GIS literacyhttps://doi.org/10.31223/X5P38PTowards understanding the spatial literacyof ChatGPT• 60 questions adapted from "GIS Fundamentals"Towards understanding the spatial literacyof ChatGPT• Fundamental concepts• Data models, datums/projections/CS, maps, data editing, GNSS, aerial/satellite• Data sources and tabular data• Digital data, attribute data• Spatial analysis• Basic analysis techniques, raster analysis, terrain analysis• Spatial statistics and interpolation• Sampling, interpolation methods, kernels estimation• Applied GIS workflow• Select the correct steps based on an objective, e.g. "Identify flat building sites, outside of the floodplain, within 1/4 mile of a road"Towards understanding the spatial literacyof ChatGPT• Results• ChatGPT would pass (this) introductory GIS examTowards understanding the spatial literacyof ChatGPT• Results• Particularly strong at answering simple questions about basic GIS concepts• Q: What type of attribute is human population (the number of people) in a U.S. county data layer?• A: a) interval/ratio (correct); b) nominal; c) ordinal• Q: Is the following statement true or false? Vector data models are often better for describing discrete themes such as counties, voting areas, or zip codes?• A: True (correct); FalseTowards understanding the spatial literacyof ChatGPT• Results• Latest GPT-4 appears to be good at selecting more complex workflows• Q: You are asked to do a spatial analysis that may include some of the following data layers:• BOUND - Study area boundary (vector polygon);• FEMA - 100-year floodplain map (vector poly);• …• Select the sequence of steps that comes closest to describing how to complete your task is to Identify flat building sites, outside of the floodplain, within 1/4 mile of a road.Towards understanding the spatial literacyof ChatGPT• Results• Not so good at questions that require calculationsQ: Assume an Earth radius of 6,278 kilometers. What is the ground surface distance, expressed in meters, of an arc that spans 14.2 seconds of arc? Please enter your answer to the nearest 0.1 meter​A: 864.4; B: 492.5; C: 432.2 (correct)​Outline• Introduction• Case Study #1: Towards Understanding the Spatial Literacy of ChatGPT• Case Study #2: Mapping with ChatGPT• Case Study #3: ChatGPT as a Mapping Assistant• Case Study #4: GeoGPTMapping with ChatGPT• Use ChatGPT as a mapping specialist• Python codes and IDE were used to interpret outputs from ChatGPT• Thematic map production includes• Graduated colored map• Batch mapping• Web maphttps://doi.org/10.3390/ijgi12070284Mapping with ChatGPT• ChatGPT can also create mental maps• Reveal the spatial thinking of AI• Outcomes include• Sketch map (right top)• Mental map (right bottom)Outline• Introduction• Case Study #1: Towards Understanding the Spatial Literacy of ChatGPT• Case Study #2: Mapping with ChatGPT• Case Study #3: ChatGPT as a Mapping Assistant• Case Study #4: GeoGPTChatGPT as a mapping assistanthttps://doi.org/10.31223/X5HQ1P• Motivation• Is generative AI useful to enrich map databases?• Can we design a low-cost, ready-to-use mapping workflow with off-the-shelf tools?ChatGPT as a mapping assistant• 3 analysts (2 undergraduate studentsand an AI analyst)https://mapillary.comChatGPT as a mapping assistant• Prompt building (Baseline scenario)• Based on the following context that was derived from a street-level photograph showing the street, recommend the most suitable tagging for an OpenStreetMap highway feature. Omit the ’oneway’ and ’lit’ tags if the answer to the corresponding questions is no or N/A. Format your suggested key-value pairs as a JSON. Your response should only contain this JSON• The content of the photograph was described as follows: A city road in an urban area along an elevated railway. There is a wide sidewalk on both sides and trees on the left. The road is mainly used by: cars. The surface of the road is: asphalt. When asked how many traffic lanes there are on the road, one would answer: 3. When asked if this street is a one-way road, one would answer: No. When asked if there are any street lights in the photograph, one would answer: Yes.ChatGPT as a mapping assistant• Prompt building• Locational context (LC)• Baseline + The photograph was taken near Downtown Miami, Florida.• Object-Detection context (OD)• Baseline + When guessing the correct category, consider that the following list of objects (separated by semicolon) are present in the photograph: Temporary barrier; Traffic light - horizontal; Traffic light - pedestrian; Signage• Object-Detection + Locational Context (OD + LC)• Baseline + OD + LC• ChatGPT's answer• {"highway": "primary", "lanes": 3}ChatGPT as a mapping assistant• Accuracy increases with the level of detail• Human analysts are better at describing images• Accuracy increases with additional context provided• Tag suggestions based on Analyst #2 achieved up to 70% accuracyChatGPT as a mapping assistant• Additional tag suggestion• "lit"="yes" 24 road segments in the dataset• One question for analysist explicitly asks if street lights are visible on the photo• First step towards generating road maps with mainly AI technologies using freely available data?Satellite or aerialRoad geometry Street level imagesRoad mapRoad attributesDeep learningObject detectionImage-to-textLLMgenerative AIOutline• Introduction• Case Study #1: Towards Understanding the Spatial Literacy of ChatGPT• Case Study #2: Mapping with ChatGPT• Case Study #3: ChatGPT as a Mapping Assistant• Case Study #4: GeoGPTGeoGPT• Motivation• Limited by model and context• Contains bias, errors, and misunderstandings• Married domain expertise with LLMs• Emhance the relevanceDomain-specific GPT tailored for GISGeoGPT• Integrate domain-specific local data with LLMs Application• Fine-tune existing LLMs• Generate vector index of the local data• Create front-end interface so that client can "chat" with the local dataUnstructured DataCode / ScriptsStructured DataLLM Application (GeoGPT)QA / ChatFine-tuneEmbeddingGeoGPT• Overall Architecture DesignUnstructured DataCode / ScriptsStructured DataGeneratingtrainingdataEmbeddingFine-tune LLMsVectorstoreExisting Chat Apps (ChatGPT, Copilot, Bard, etc.)OutputsPromptsContextLLMs< Answer >< Questions >< Query >< Parse >AggregateRankMergeGeoGPT• Chatbot with domain knowledge (embedding gis book + gpt 3.5)https://www.ucgis.org/gis-t-body-of-knowledgehttps://spatialanalysisonline.comGeoGPT• Chain different agent (searching)• Prioritize on local data• Controllable outputGeoGPT• Future worksVirtual Assistant / Tutor Domain Specific Model AI Data MinerEducation & ServicesDomain Knowledge Extension & EnrichExisting Data Discovery & ExplorationOutline• Introduction• Case Study #1: Towards Understanding the Spatial Literacy of ChatGPT• Case Study #2: Mapping with ChatGPT• Case Study #3: ChatGPT as a Mapping Assistant• Case Study #4: GeoGPT• SummarySummary• Generative AI and LLMs are here• A lot of potential to help GIS workflows• Coding, designing workflows, generating maps, assisting analysts, etc.More research is needed to harness full potential, understand limitations, biases, etc.Thank you!!Questions??    ljuhasz@fiu.edu    bguan@fiu.edu      https://maps.fiu.edu/    @fiugis

Utilizing Large Language Models in geographic contexts - Experiences from the FIU GIS Center

https://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=1104&context=gis

Utilizing Large Language Models in geographic contexts - Experiences from the FIU GIS Center

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