Interactive Geospatial Intelligence: Querying the Earth

Interactive<br>Geospatial Intelligence

How modern systems turn imagery into real-time answers

SECTION 1 — HOOK

The Real Problem

Too much imagery

Too little time

Manual analysis doesn't scale

SECTION 1 — HOOK

What's Changed Recently

Before

More data.<br>Same slow analysis.

Now

Interactive systems.<br>Real-time answers.

ANALYSIS

INTERACTION

SECTION 1 — HOOK

The New Paradigm

You don't analyse imagery anymore.

You query it.

DRAW

SEARCH

REFINE

SECTION 2 — THE CORE IDEA

What Enables This

3 building blocks

Tiling

Break imagery into manageable chunks at scale

Feature Extraction

Teach machines to understand what they see

Similarity Search

Find matching patterns at speed

SECTION 2 — THE CORE IDEA

The Mental Model

Google for pixels.

Drawing / Example

System

Similar pixels everywhere

SECTION 2 — THE CORE IDEA

CAPABILITY SPOTLIGHT #1

Real-Time Feature Extraction

Turn imagery → structured data instantly

Extract buildings, roads, features automatically

Works at planetary scale

Structured output from raw pixels

Example

AI-powered semantic 3D mapping of the entire Earth

SECTION 3 — PLAYER SPOTLIGHTS

CAPABILITY SPOTLIGHT #2

All-Weather, Always-On Sensing

See what others can't

<strong>SAR</strong> = Synthetic Aperture Radar (not optical)

Penetrates clouds, works at night

Persistent coverage regardless of conditions

Example

World's largest SAR satellite constellation

SECTION 3 — PLAYER SPOTLIGHTS

CAPABILITY SPOTLIGHT #3

Large-Scale Geospatial ML Platforms

Run models across the planet

Execute ML pipelines across massive global datasets

Time-series analysis across months/years

Global coverage, cloud-native

Example

Planetary-scale geospatial analytics platform

SECTION 3 — PLAYER SPOTLIGHTS

CAPABILITY SPOTLIGHT #4

Economic & Activity Detection<br>from Space

Infer behaviour, not just objects

<b style="color: white; font-weight: 600;">Detect cars in parking lots</b> &rarr; measure retail activity

<b style="color: white; font-weight: 600;">Count ships in ports</b> &rarr; track trade flows

<b style="color: white; font-weight: 600;">Read economic patterns from orbit</b>

Geospatial analytics for economic intelligence

SECTION 3 — PLAYER SPOTLIGHTS

CAPABILITY SPOTLIGHT 5 — THE UPGRADE

Multi-Sensor Intelligence

Fusion of different data types

Radio frequency RF signals from space

Combined with optical imagery

Detect what you cannot see, only hear

Multi-layer intelligence fusion

Example

RF signal geolocation plus imagery fusion

SECTION 3 PLAYER SPOTLIGHTS

Shared Architecture Pattern

Every major geospatial intelligence platform does this:

INGEST

Massive data intake

TILE

Break into manageable chunks

EXTRACT

Run feature models

STORE

Fast-access vector storage

QUERY

Real-time retrieval

This is a data engineering problem as much as an AI problem.

SECTION 4 — WHAT THEY HAVE IN COMMON

The Real Innovation

It's not the models.

PRECOMPUTATION

Do the heavy work ahead of time

SMART STORAGE

Structure data for instant retrieval

FAST RETRIEVAL

Sub-second query at global scale

SECTION 4 — WHAT THEY HAVE IN COMMON

Minimal System You Could Build

A modular, composable pipeline — starting today

SECTION 5 — HOW YOU WOULD BUILD THIS

Modular. Composable. Buildable now.

STORE

Cloud-Optimized<br>GeoTIFF (COG) on S3

TILE

Slice into<br>XYZ tiles

EXTRACT

Run model -><br>generate embeddings

INDEX

Store vectors<br>in FAISS

QUERY

Expose via<br>REST API

You don't need satellites<br>to innovate here.

Use open imagery

Build small prototypes

Focus on interaction

The opportunity is in UX + systems, not just data.

SECTION 5 — FINAL THOUGHT