Organizations have used geospatial machine studying (ML) for property danger evaluation, catastrophe response, and infrastructure planning. These techniques labored nicely however couldn’t scale past specialised use instances. Every query required a number of geospatial datasets, every with its personal mannequin and sometimes its personal workflow, limiting these capabilities to a handful of high-value use instances on the largest enterprises that would afford the funding. On this publish, you’ll learn to deploy geospatial AI brokers that may reply advanced spatial questions in minutes as an alternative of months. By combining Foursquare Spatial H3 Hub’s analysis-ready geospatial knowledge with reasoning fashions deployed on Amazon SageMaker AI, you possibly can construct brokers that allow nontechnical area consultants to carry out refined spatial evaluation via pure language queries—with out requiring geographic info system (GIS) experience or customized knowledge engineering pipelines.
Geospatial intelligence adoption boundaries
Two technical boundaries have prevented these specialised geospatial techniques from reaching broader adoption. First, geospatial knowledge arrives in a bewildering array of codecs—satellite tv for pc imagery saved as GeoTIFF rasters, administrative boundaries saved as shapefile vectors, climate fashions saved as NetCDF grids, and property data in proprietary cadastral codecs—every requiring totally different parsing libraries and customized knowledge pipelines. Second, becoming a member of datasets throughout spatial granularities is nontrivial: property insurance coverage knowledge geocoded to particular person addresses should mix with local weather danger knowledge at 1 km grid cells and census demographics aggregated to dam teams, requiring organizations to spend months constructing customized processing pipelines earlier than answering their first enterprise query. In brief, there is no such thing as a common be part of key to mix these datasets. This implies organizations can’t experiment with geospatial intelligence with out first constructing knowledge engineering pipelines to normalize numerous codecs, implement spatial processing for coordinate transformations and determination resampling, and deploy specialised computing infrastructure.
Fixing technical boundaries alone wasn’t enough. Earlier techniques nonetheless required 6–12 month implementations with specialised GIS groups. 5 enterprise necessities remained unaddressed: making geospatial evaluation accessible to nontechnical area consultants, displaying how AI reaches conclusions, supporting versatile evaluation, delivering interactive response instances, and providing price predictability at scale.
Three applied sciences converging to handle adoption challenges
Addressing these technical and enterprise boundaries requires a essentially totally different strategy. This structure combines three applied sciences to handle these gaps:
- Foursquare Spatial H3 Hub for analysis-ready knowledge – This service transforms inaccessible raster and vector geospatial knowledge into analysis-ready options, listed to the H3 hierarchical grid system, in tabular format that knowledge scientists can question utilizing acquainted instruments comparable to Spark, Python, and DuckDB. Datasets containing latitude and longitude coordinates, metropolis names, or zip codes may be simply enriched by becoming a member of on a typical H3 cell, eliminating months of information preparation and specialised GIS experience.
- Reasoning fashions and agentic AI for adaptive workflows – Fashions comparable to DeepSeek-R1 and Llama 3 break down advanced issues, purpose via multistep workflows, and orchestrate actions throughout knowledge sources. They dynamically decide which datasets to mix and plan analytical sequences that beforehand required GIS experience—reworking static, preconfigured workflows into adaptive reasoning techniques.
- Amazon SageMaker AI for cost-effective generative AI inference – This Amazon SageMaker AI functionality supplies managed infrastructure for deploying open supply fashions with optimized inference runtimes, auto scaling, and operational tooling. Groups can concentrate on constructing geospatial intelligence capabilities fairly than managing underlying infrastructure.
Collectively, these applied sciences allow organizations to entry analysis-ready geospatial knowledge, deploy adaptive reasoning brokers, and run manufacturing inference with out constructing specialised infrastructure. On this publish, we exhibit a manufacturing geospatial agent that mixes Foursquare Spatial H3 Hub with reasoning fashions deployed on Amazon SageMaker AI.
Evaluation-ready geospatial knowledge with Foursquare Spatial H3 Hub
Foursquare’s Spatial H3 Hub eliminates conventional geospatial adoption boundaries via a proprietary H3 indexing engine. This engine has reworked dozens of disparate geospatial datasets into an Iceberg catalog prepared for quick evaluation, changing months of information engineering with prompt entry to analysis-ready geospatial options.
The H3 indexing engine addresses the foundation explanation for geospatial complexity: the huge array of codecs and coordinate techniques which have traditionally restricted entry to geographic info. The engine converts spatial knowledge, raster imagery, or vector datasets by indexing it into the H3 hierarchical spatial grid at international scale. H3 divides the whole Earth into nested hexagonal cells, making a common grid system the place each location has a standardized identifier. The engine extracts knowledge from raster pictures or numerous vector shapes comparable to census tract polygons and converts them into options connected to H3 cell IDs in tabular format, the place the cell ID turns into a common be part of key that abstracts away format complexity and coordinate techniques. An insurance coverage firm’s property knowledge, Nationwide Oceanic and Atmospheric Administration (NOAA) local weather projections, census demographics, and infrastructure networks can all be mixed as a result of they share this frequent spatial index.
The engine additionally handles the methodological complexities that historically required GIS experience. It may well index knowledge to H3 cells at any precision from decision 0 (about 1,000 km hexagons protecting continents) right down to decision 15 (about 1 meter hexagons protecting particular person buildings). You’ll be able to select the suitable decision for every use case—coarser resolutions for regional local weather evaluation, finer resolutions for property-level evaluation. When boundaries don’t align completely—like a census tract overlapping a number of H3 hexagons—the engine intelligently handles partial overlaps via both quick centroid-based approximation or actual proportional allocation primarily based on intersection areas. It additionally robotically aggregates or disaggregates knowledge when combining datasets at totally different scales, eliminating the guide preprocessing that historically consumed months of GIS specialist time.
Constructed on this indexing basis, Foursquare Spatial H3 Hub delivers an Iceberg catalog containing datasets spanning power infrastructure, environmental situations, and pure hazards all initially in numerous raster and vector codecs, now pre-indexed to H3 cells at decision 8 (with further resolutions accessible on demand). You’ll be able to question this knowledge with acquainted instruments comparable to SQL, Python, Spark, Snowflake, and Databricks with out proprietary GIS software program. H3 cell identifiers turn out to be easy column values that be part of like some other attribute, so you possibly can quickly validate geospatial hypotheses by becoming a member of their proprietary knowledge with Foursquare’s H3 catalog.
Reasoning fashions for spatial Intelligence
Reasoning fashions comparable to DeepSeek-R1 change how AI handles geospatial intelligence. Conventional geospatial techniques operated as collections of static, purpose-built fashions, with separate fashions for flood danger, wildfire publicity, and earthquake vulnerability. Every mannequin was educated on particular datasets and incapable of answering questions outdoors its slender area. When necessities shifted or new knowledge emerged, organizations confronted months of retraining. Reasoning fashions change this paradigm by decomposing advanced issues, planning multistep workflows, and orchestrating actions throughout knowledge sources dynamically. Somewhat than requiring pre-trained fashions for each query, these techniques purpose via novel eventualities by combining accessible knowledge in methods by no means explicitly programmed. Requested “which neighborhoods face compounding local weather and financial dangers?”, a reasoning agent determines it wants flood publicity knowledge, family revenue, property density, and neighborhood boundaries after which executes that analytical pipeline by calling acceptable instruments and knowledge sources. The agent understands spatial relationships conceptually: level knowledge aggregates to polygons, grid cells map to administrative boundaries, proximity requires acceptable distance metrics. At every step, it causes about what info comes subsequent and adjusts when knowledge reveals surprising patterns, reworking geospatial evaluation from pre-scripted queries into adaptive investigation.
Deploying brokers on Amazon SageMaker AI
Evaluation-ready geospatial knowledge and reasoning-capable fashions resolve vital components of the puzzle, however manufacturing deployment creates new challenges. Geospatial brokers want sustained inference capability to course of queries, execute reasoning chains, retrieve knowledge, and generate visualizations. Organizations face a selection: construct customized inference infrastructure with GPU clusters, load balancers, and auto scaling insurance policies, or depend on industrial massive language mannequin (LLM) APIs the place prices scale unpredictably with utilization and knowledge governance turns into advanced.
Amazon SageMaker AI supplies managed infrastructure for deploying and working open supply generative AI fashions in manufacturing. You’ll be able to deploy fashions from Hugging Face or Amazon SageMaker AI JumpStart—together with reasoning fashions comparable to DeepSeek-R1, Llama 3, or Qwen—to SageMaker AI real-time or asynchronous inference endpoints with out managing underlying infrastructure. Amazon SageMaker AI Inference handles occasion provisioning, helps optimized serving runtimes like vLLM and SGLang, and supplies auto scaling primarily based on site visitors patterns.
Amazon SageMaker AI Inference capabilities handle a number of operational challenges particular to agent architectures. Geospatial brokers dealing with variable question masses all through the day profit from automated scaling on GPU cases comparable to G5, P4d, and P5 primarily based on request quantity or customized metrics. Lengthy-running spatial analyses that exceed typical API timeouts can path to asynchronous inference endpoints the place SageMaker AI queues request, course of them, and ship outcomes to Amazon Easy Storage Service (Amazon S3), enabling advanced multi-dataset analyses with out client-side timeout points. For architectures using a number of fashions, multi-container endpoints host totally different fashions on shared infrastructure with unbiased scaling insurance policies and site visitors routing. Constructed-in integration with Amazon CloudWatch for monitoring, AWS Identification and Entry Administration (IAM) for entry management, and Amazon Digital Personal Cloud (Amazon VPC) for community isolation simplifies operational necessities.
Foursquare Spatial H3 Hub and Amazon SageMaker AI collectively scale back operational complexity. Knowledge scientists can concentrate on constructing agent capabilities, defining which H3 Hub datasets to question for particular questions, refining prompting methods for spatial reasoning, and optimizing tool-calling patterns fairly than managing underlying infrastructure. Organizations may experiment with totally different open supply fashions. Such initiatives, which beforehand required separate groups for knowledge engineering, mannequin improvement, and platform operations, have now turn out to be accessible to smaller groups with out specialised infrastructure experience.
Designing the Foursquare Spatial Agent
The Foursquare Spatial Agent structure combines reasoning fashions deployed on SageMaker AI with tool-calling capabilities that question Foursquare Spatial H3 Hub instantly. The agent orchestrates the whole workflow from pure language query to visualization with out guide intervention.
Agent workflow
When a person poses a pure language query about spatial relationships—comparable to “Which neighborhoods in Los Angeles face each excessive flood danger and financial vulnerability?”—the agent executes a multistep reasoning course of. The reasoning mannequin first analyzes the query and identifies required info: flood danger scores, financial indicators like revenue and employment, and neighborhood boundaries. It then determines which H3 Hub datasets include related info by reasoning over dataset descriptions. With datasets chosen, the mannequin calls H3 Hub question instruments, developing SQL queries that be part of datasets on H3 cell IDs. After executing these queries, the mannequin analyzes outcomes to establish spatial patterns and statistical relationships. Lastly, it generates Vega specs for charts and Kepler.gl specs for maps that visualize the findings.
This workflow makes use of the reasoning mannequin’s capability to plan, adapt, and get better from errors. If preliminary queries return surprising outcomes, the mannequin can refine its strategy, choose further datasets, or alter spatial operations—capabilities of that static, preprogrammed workflow.
Design choices addressing enterprise necessities
Constructing a manufacturing geospatial agent required addressing the 5 enterprise necessities recognized via deployment evaluation. Three key design choices illustrate how the structure balances accessibility, transparency, and adaptability.
Insurance coverage underwriters perceive flood danger and property publicity however don’t write SQL or Python. The agent structure makes geospatial evaluation accessible by accepting pure language questions and translating them into acceptable H3 Hub queries. The reasoning mannequin interprets domain-specific terminology like “susceptible neighborhoods” or “high-risk areas” and maps these ideas to related datasets and analytical operations. This eliminates the bottleneck the place area consultants should submit evaluation requests to knowledge groups, enabling self-service exploration.
Area consultants additionally want to grasp how the agent arrived at conclusions, particularly when analyses inform enterprise choices. The agent can log its reasoning course of at every step: which datasets have been thought-about and why, what spatial operations have been deliberate, which queries have been executed, and the way outcomes have been interpreted. Each visualization contains metadata displaying which H3 cells and supply datasets contributed to the evaluation. This transparency means customers can validate the agent’s analytical strategy and perceive the info sources behind conclusions. If an insurance coverage underwriter sees a high-risk evaluation for a property, they’ll hint again via the reasoning chain to see it mixed flood publicity knowledge from Federal Emergency Administration Company (FEMA), wildfire danger from state forestry knowledge, and property traits from native assessor data—constructing confidence in AI-generated insights. Implementation makes use of structured logging to seize reasoning steps, making the agent’s decision-making course of inspectable and debuggable fairly than a black field.
Pre-built dashboards serve identified questions however fail when analysts must discover variations. The agent structure supplies flexibility through the use of tool-calling to dynamically compose analyses. Somewhat than predefining workflows for each situation, the reasoning mannequin determines which H3 Hub datasets to question and tips on how to mix them primarily based on the particular query. This permits the agent to deal with unexpected analytical questions with out requiring new engineering work for every variation. The agent makes use of perform calling APIs supported by fashions comparable to Llama 3 and DeepSeek-R1 to work together with H3 Hub. The mannequin receives device descriptions specifying accessible datasets, question parameters, and return codecs, then constructs acceptable device calls throughout reasoning. SageMaker AI endpoints deal with the inference, whereas customized software logic manages device execution and end result meeting.
SageMaker AI deployment structure
The Foursquare Spatial Agent deploys on SageMaker AI real-time inference endpoints with configuration optimized for manufacturing geospatial workloads. The deployment makes use of G5 cases comparable to g5.2xlarge for improvement and g5.12xlarge for manufacturing, offering cost-effective GPU inference for fashions within the 7B–70B parameter vary generally used for agent reasoning. A goal monitoring scaling coverage primarily based on the InvocationsPerInstance metric maintains response instances throughout variable load whereas minimizing prices throughout low-traffic durations. Spatial analyses involving massive geographic extents or many datasets be part of path to asynchronous inference endpoints, permitting queries that may take 60 seconds or extra to finish with out exceeding typical API timeout limits whereas sustaining responsive habits for extra easy queries.
CloudWatch metrics monitor inference latency, error charges, and token throughput throughout the deployment. Customized metrics log reasoning chain depth, variety of device calls per question, and dataset entry patterns, enabling steady optimization of agent efficiency. This deployment structure supplies production-grade reliability whereas sustaining flexibility for experimentation with totally different fashions and prompting methods.
Foursquare Spatial Agent in motion
The next demonstrations present how organizations throughout insurance coverage, banking, and concrete planning can use this functionality to reply advanced spatial questions in minutes—collapsing timelines that beforehand stretched throughout quarters into interactive workflows accessible to area consultants with out specialised technical abilities. In insurance coverage danger evaluation, the agent predicts which areas within the Los Angeles area are prone to witness elevated insurance coverage charges by computing a composite danger rating from flood danger, hearth hazard severity, crime charges and the FEMA nationwide danger index datasets at totally different spatial resolutions and codecs, now queryable via frequent H3 cell IDs. An underwriter asks the query in pure language, and the agent handles dataset choice, spatial joins, danger aggregation, and map visualization with out requiring GIS experience.
For banking market evaluation, the agent supplies a 360-degree view of Los Angeles’s financial institution community planning. It combines demographic knowledge together with inhabitants, revenue, and age distribution with healthcare facility places, crime statistics, and factors of curiosity to establish under-served markets and enlargement alternatives. This evaluation informs data-driven choices for department placement, product focusing on, and monetary inclusion initiatives. Beforehand, assembling these datasets and performing spatial evaluation required weeks of GIS specialist time. Now, the agent delivers leads to minutes via conversational interplay.
For city infrastructure planning, the agent helps town of Chandler, Arizona, plan sustainable city improvement over the subsequent decade. It combines inhabitants progress projections, housing improvement patterns, median revenue developments, and infrastructure knowledge together with buildings, energy strains, and cell towers—all listed to H3 cells. City planners discover eventualities by asking questions like “which areas will expertise inhabitants progress however lack enough infrastructure?” The agent causes via the analytical necessities, executes acceptable spatial queries, and generates visualizations displaying infrastructure gaps that want funding.
The democratization of geospatial intelligence
Foursquare Spatial H3 Hub, reasoning fashions, and Amazon SageMaker AI collectively take away the boundaries. Organizations can now entry standardized geospatial knowledge, deploy reasoning brokers with tool-calling capabilities, and run manufacturing inference with out constructing specialised infrastructure.
To deploy geospatial AI brokers:
- Entry Foursquare Spatial H3 Hub for analysis-ready datasets.
- Deploy reasoning fashions on Amazon SageMaker AI with SageMaker JumpStart or Hugging Face.
- Construct agent capabilities that join fashions to H3 Hub datasets via tool-calling.
Concerning the authors
Vikram Gundeti presently serves because the Chief Expertise Officer (CTO) of Foursquare, the place he leads the technical technique, choice making, and analysis for the corporate’s Geospatial Platform. Earlier than becoming a member of Foursquare, Vikram held the place of Principal Engineer at Amazon, the place he made his mark as a founding engineer on the Amazon Alexa workforce.
Amit Modi is a Senior Supervisor of Product Administration at Amazon SageMaker AI, the place he focuses on ModelOps and Inference. His evaluation of enterprise adoption patterns and design of the SageMaker deployment strategy described on this publish emerged from work with enterprise prospects.
Aditya Badhwar is a Senior Options Architect at AWS primarily based out of New York. He works with prospects offering technical help and architectural steerage on varied AWS providers. Previous to AWS, Aditya labored for over 16 years in software program engineering and structure roles for varied large-scale enterprises.
