Satellite imagery object detection using text prompts
I built a browser-based tool for detecting objects in satellite imagery using vision-language models (VLMs). You draw a polygon on the map and enter a text prompt such as "swimming pools", "oil tanks", or "buses". The system scans the selected area tile-by-tile and returns detections projected back onto the map as GeoJSON.Pipeline: select area and zoom level, split the region into mercantile tiles, run each tile with the prompt through a VLM, convert predicted bounding boxes to geographic coordinates (WGS84), and render the results back on the map.It works reasonably well for distinct structures in a zero-shot setting. occluded objects are still better handled by specialized detectors like YOLO models.There is a public demo and no login required. I am mainly interested in feedback on detection quality, performance tradeoffs between VLMs and specialized detectors, and potential real-world use cases.
FL score
out of 100
Verdict
high confidence
Competition
6
competitors found, emerging market, funded players
Trend
1 community mentions
A browser-based tool for text-prompt object detection in satellite imagery, targeting the critical gap of VLM limitations with dense/occluded objects for enterprise users.
The pain
The gap
Build angle
Strengths
- Addresses a specific, acknowledged limitation of current VLM models (dense/occluded objects).
- Large and rapidly growing geospatial AI market with significant spending.
- The zero-shot text-prompt approach offers high user convenience.
- Clear B2B value proposition for industries needing accurate geospatial insights.
Risks
- High competition from well-funded startups and established players (Esri, Landing AI, Picterra).
- Technical complexity for a solo builder to develop and scale a robust hybrid VLM+specialized detector system.
- Enterprise B2B sales cycles can be long and challenging for a solo founder.
- Achieving superior accuracy and performance compared to specialized models is a significant technical hurdle.
- High infrastructure and compute costs for processing large satellite imagery datasets.
- Limited evidence of specific willingness to pay for this *exact* hybrid solution, beyond general market demand.
Next steps
- Conduct in-depth interviews with potential users in specific industries (e.g., disaster response, energy, insurance) to validate the severity of the occluded object problem and their specific workflow frustrations.
- Identify the 'one specific, hard-to-detect object' in a 'specific scenario' that the hybrid model can demonstrably outperform existing solutions on.
- Develop a detailed cost model for running the detection pipeline at scale to ensure economic viability for a solo builder.
- Explore partnerships or integrations with existing geospatial platforms to leverage distribution and data rather than building everything from scratch.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 25
- Solution gap
- 17
- Willingness to pay
- 17
- Buildability
- 8
Strong problem clarity with a clear gap in VLM accuracy for specific object types, but the market is crowded with well-funded competitors, and the buildability for a solo founder is challenging.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
A growing market with a clear pain, but challenges in competitive differentiation and solo execution.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Clear problem with potential monetization, but significant challenges for a solo founder in execution, audience reach, and maintaining simplicity.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Clear value proposition for a specific pain, but significant risks in assumption validation, distribution, and solo execution in a competitive market.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Real demand and a clear specific problem, but needs a narrower focus and evidence of solving the hardest parts of the problem better than existing solutions.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Conduct in-depth interviews with potential users in specific industries (e.g., disaster response, energy, insurance) to validate the severity of the occluded object problem and their specific workflow frustrations.
- Identify the 'one specific, hard-to-detect object' in a 'specific scenario' that the hybrid model can demonstrably outperform existing solutions on.
- Develop a detailed cost model for running the detection pipeline at scale to ensure economic viability for a solo builder.
- Explore partnerships or integrations with existing geospatial platforms to leverage distribution and data rather than building everything from scratch.
Who is already there, emerging market
Esri integrates vision-language models into its ArcGIS platform for geospatial analysis, allowing users to extract features from imagery using natural language prompts for tasks like object detection and segmentation.
Pricing: Contact vendor for pricing (ArcGIS platform pricing varies, VLMs are integrated capabilities).
VisionAgent offers reasoning-driven object detection using text prompts without custom training, leveraging AI agents to understand object attributes, relationships, and dynamic states.
Pricing: Available via API, processing takes 20-30 seconds per image (working on speed improvements). Contact vendor for specific API pricing.
Element 84 utilizes remote sensing vision-language models like SkyCLIP and RemoteCLIP to enable 'queryable Earth' functionality, allowing retrieval of images and geolocations using text queries over large geographical areas.
Pricing: Information not readily available, likely custom solutions or enterprise-focused.
Picterra is a cloud-based geospatial intelligence platform that allows users to detect objects in satellite and aerial imagery, primarily through training custom detectors.
Pricing: Contact vendor for pricing; often offers different tiers based on usage and features.
Sinergise develops enterprise-level solutions for managing spatial data and processing satellite imagery, with Sentinel Hub being a key platform for accessing and processing Earth observation data.
Pricing: Offers various plans, including a free tier for basic usage, and paid plans based on data volume and services (e.g., €30/month for advanced features).
Hasty.ai provides an AI-powered annotation platform for computer vision teams to build, fine-tune, iterate, and manage AI models faster with high-quality training data.
Pricing: Contact vendor for pricing; offers a free trial.
What they charge
What people say, 1 mentions
I built "Google Lens for teams" – Search and ask anything about 1000s of your photos & videos – a ChatGPT for your photos and videos
r/SaaS
Recent news
Show HN: Satellite imagery object detection using text prompts
Hacker News, March 11 2026
Show HN: Detect any object in satellite imagery using a text prompt
Hacker News, March 12 2026
VisionAgent: Reasoning-Driven Agentic Object Detection
Product Hunt, February 12 2025
Talk to Your Imagery: Vision-Language Models for Geospatial Analysis
Esri, August 14 2025
Building a queryable Earth with vision-language foundation models
Element 84, March 19 2024
Market signals
The geospatial artificial intelligence market is a rapidly growing market, valued at $38 billion in 2023 and projected to reach $64.60 billion by 2029, with some reports indicating a potential increase to $126.58 billion by 2035. Key drivers include increased deployment of AI-driven geospatial solutions for real-time decision-making in urban planning, defense, agriculture, and disaster management. Recent trends highlight the adoption of cloud-based platforms and the integration of GeoAI with IoT and edge computing for real-time insights.
What frustrates people
- Users of Picterra look for alternatives due to pricing concerns and missing features.
- Picterra alternatives are sought for better integration and deployment, and improved customer support.
- General complaints for Hasty.ai alternatives suggest a need for better AI knowledge bases, writing tools, and customer service rather than core object detection capabilities.
- Some users find the maximum polygon size on mobile to be very small in a browser-based VLM tool for object detection.
- Zero-shot VLM models for satellite imagery object detection can struggle with dense or occluded objects, where specialized detectors like YOLO models still perform better.
Semble – Code search for agents that uses 98% fewer tokens than grep
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Postgres extension for BM25 relevance-ranked full-text search
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Consus (YC F24) – LLM gateway for companies handling sensitive data
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