How We Built a Geospatial AI That Reads Satellite Images

A developer’s guide to analyze_image, analyze_text, and the future of location intelligence.

The motivation

You’re a developer. You know that satellite imagery is everywhere. But most GeoAI solutions are either:

Black‑box desktop tools with 500 buttons
Intelligence‑grade surveillance systems you’d never touch
Overhyped pixel classifiers that can’t tell a port from a parking lot

We wanted something different:

A code‑first, ethical, and actually useful geospatial AI that runs in a Jupyter notebook.

So we built it with:

arcgis.ai.analyze_image – to read satellite photos
arcgis.ai.analyze_text – to write structured reports

A few dozen lines of Python, and ArcGIS Location Platform (beta AI)

No mouse clicks. Just code.

In this post, I’ll show you exactly how it works – including real snippets from our platform.py – so you can steal the ideas (or the whole notebook) for your own projects.

The Core Idea (Super Simple)

User pans a map to any location.
We export that exact view as a georeferenced PNG.
We send the image + a strict, no‑speculation prompt to our beta AI endpoint using analyze_image.
The AI returns a natural‑language description.
We feed that description + location data into analyze_text with a report template.
Out comes a clean Markdown report (Location, Features, Patterns, Risks, Assessment).

That’s it. No complex pipelines. No training custom models. Just clever prompting and two AI functions.

The Code You Actually Care About

Our entire workflow is in platform.py. Let me walk you through the juicy parts.

Export a map image (no screenshot hacks)

# author: Jan Tschada
# SPDX-License-Identifer: Apache-2.0

def export_map(map_view) -> ExportedMapImage:
    bbox = f"{map_view.extent['xmin']},{map_view.extent['ymin']},{map_view.extent['xmax']},{map_view.extent['ymax']}"
    bbox_sr = map_view.extent['spatialReference']['wkid']
    map_layer = MapImageLayer("https://services.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer")
    return ExportedMapImage(
        map_export_result=map_layer.export_map(bbox=bbox, bbox_sr=bbox_sr, image_format="png"),
        bbox=bbox, bbox_sr=bbox_sr
    )

Why this is cool:

The exported image is georeferenced, so we know exactly which coordinates each pixel covers. That means we can later tell the AI: "You’re looking at 25.02° N, 55.04° E – the Port of Jebel Ali."

Download image bytes (streaming, memory‑efficient)

# author: Jan Tschada
# SPDX-License-Identifer: Apache-2.0

def download_bytes(url, timeout=10):
    response = requests.get(url, stream=True, timeout=timeout)
    response.raise_for_status()
    file_bytes = io.BytesIO()
    for chunk in response.iter_content(chunk_size=8192):
        if chunk:
            file_bytes.write(chunk)
    file_bytes.seek(0)
    return file_bytes

Nothing fancy, but it’s solid. We stream the image in 8KB chunks so we don’t blow up memory on huge exports.

Reverse geocode the image center

# author: Jan Tschada
# SPDX-License-Identifer: Apache-2.0

gps_extent = project([exported_image.map_export_result["extent"]], in_sr=bbox_sr, out_sr=4326)[0]
[xmin, ymin, xmax, ymax] = gps_extent.coordinates()
gps_lon = xmin + 0.5 * (xmax - xmin)
gps_lat = ymin + 0.5 * (ymax - ymin)

geocoding_result = reverse_geocode({"x": gps_lon, "y": gps_lat, "spatialReference": {"wkid": 4326}}, lang_code="EN")
address = geocoding_result.get("address")

Now we have a human‑readable address (e.g., "Port of Jebel Ali, Dubai, UAE") to feed into the AI prompts.

The secret sauce: a disciplined prompt for analyze_image

This is where most people mess up. They ask, "Tell me everything about this place." Then the AI hallucinates.

We do the opposite:

# author: Jan Tschada
# SPDX-License-Identifer: Apache-2.0

ai_prompt = f"""
    You are a geospatial imagery analyst.
    Analyze the satellite image using only visible evidence.
    State uncertainty explicitly. Do not speculate.

    Location: {gps_lat}, {gps_lon}
    {address.get('LongLabel') if address else ''}
"""

The rules:

Only what you see
If unsure, say so
No guessing

This dramatically reduces hallucinations. The AI will say "I see what looks like a port, but I cannot confirm the exact type" instead of inventing a naval base.

Call analyze_image

# author: Jan Tschada
# SPDX-License-Identifer: Apache-2.0

image_base64 = f"data:image/png;base64,{base64.b64encode(image_bytes.getvalue()).decode('utf8')}"
analyze_image_result = analyze_image(image=image_base64, prompt=ai_prompt, gis=portal, to_language="EN")

That’s it. One line. The gis instance is your authenticated ArcGIS portal connection.

Generate a structured report with analyze_text

We take the image analysis output and wrap it into a report template.

# author: Jan Tschada
# SPDX-License-Identifer: Apache-2.0

ai_summarize_prompt = f"""
    You are a senior geospatial analyst.
    Create a professional situational analysis report.
    Output MUST be valid Markdown.
    Sections:
    ## Location
    ## Observed Features
    ## Activity & Patterns
    ## Risk & Threat Assessment
    ## Analyst Assessment
    Write concisely, analytically.
"""

intel_data = {
    "image_analysis": analyze_image_result.results,
    "location": {"latitude": gps_lat, "longitude": gps_lon, "address": address}
}

analyze_text_result = analyze_text(text=str(intel_data), prompt=ai_summarize_prompt, gis=portal, to_language="EN")

Boom. The second AI reorganises the raw description into a clean, sectioned report. No manual parsing required.

Here’s what the AI actually produced for one of our test ports:

Situational Analysis Report - Date 2026-04-20T10:15:32

Location

Geocoordinates: 25.0137 N, 55.0743 E
Identified Site: Mena Jabal Ali, Dubai, United Arab Emirates
Area Overview: Situated in the Jebel Ali district of Dubai, encompassing significant coastal, urban, and artificial landforms.

Observed Features

Prominent artificial island resembling a palm tree (Palm Jebel Ali).

Urban coastal infrastructure comprising dense road networks, residential zones, and potential industrial facilities.

Multiple marinas and docks located along the shoreline, enhancing maritime accessibility.

Visible boundaries distinguishing developed urban tracts from adjacent desert areas.

Pockets of sparse vegetation and undeveloped open land interspersed throughout the region.

Activity & Patterns

Extensive urban development and engineered expansions, typified by the artificial island and substantial coastal modification, suggest large-scale planning and ongoing construction activity.

The presence of marinas and docks points to active maritime transit and probable commercial shipping or recreational boating.

The orderly distribution of residential and industrial areas implies strategic urban zoning.

Noticeable demarcations between green/open land and developed sectors reflect active land management and expansion.

Risk & Threat Assessment

The concentration of critical urban and maritime infrastructure in a narrow coastal corridor increases vulnerability to natural hazards (e.g., sea-level rise, storms) and potential deliberate disruptions.

High-profile artificial landforms may attract heightened attention and pose elevated operational and maintenance risks.

The juxtaposition of industrial, residential, and transport nodes requires robust monitoring to mitigate economic or environmental threats.

Analyst Assessment

Palm Jebel Ali and the surrounding Jebel Ali area represent a nexus of strategic urban expansion, maritime operations, and infrastructural development within Dubai. The imagery confirms sustained investment in coastal engineering and the diversification of land use.

While the overall stability of the site appears high, continued monitoring is recommended to track further urban growth, shifting coastal dynamics, and associated risk profiles, given the region’s prominence and developmental trajectory.

Further detail regarding specific facility functions, population density, or economic assets is not ascertainable from imagery alone.

What’s Coming Next (Roadmap – and we need your help)

Our current version only looks at the image. But an image doesn’t tell you everything.

We plan to integrate:

OpenStreetMap – roads, building footprints, land use, ports
Wikidata – facts about the place (population, founding year, official name)

Then we’ll feed that structured data into analyze_text alongside the image analysis. Imagine the AI saying:

"I see a large building. OSM says it’s a hospital. Wikidata tells me it was built in 1985 and has 200 beds."

That’s real context‑aware geospatial AI.

We’d love your input:

Which OSM tags are most valuable for your use cases?
How would you improve the prompt to reduce hallucinations further?
Would you use this in production? Why / why not?

We’re not building a black‑box image intelligence tool. We’re building an open, ethical, developer‑friendly geospatial AI.

If that resonates with you:

Comment below with your craziest geospatial AI idea
Share this post with one colleague who might find it useful

Let’s make geospatial intelligence accessible to every developer.

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