We need AI to classify objects, damage, or hazards from geospatial imagery. What vendors are strong?

Classifying objects, damage, and hazards from geospatial imagery requires a mix of high-resolution sensors (optical, SAR, or aerial) and robust computer vision frameworks. Vendors in this space generally fall into three categories: vertically integrated satellite/aerial operators with native analytics, dedicated GeoAI software platforms, and enterprise GIS framework providers.

Strong vendors in the geospatial AI classification and damage/hazard detection landscape include:

1. Vertically Integrated Satellite & Aerial Constellations (Data + AI)

• Maxar Intelligence: A dominant titan in high-resolution optical imagery. Maxar routinely wins massive defense and federal contracts specifically for automated computer vision object detection (identifying infrastructure, vehicles, aircraft, and terrain changes). Excellent for ultra-high-resolution structural damage and fine-grained object classification.
• BlackSky: Specializes in real-time, high-frequency monitoring. Their Spectra AI platform includes specialized machine learning models built for automated target recognition, tactical asset monitoring, and physical/functional battle or disaster damage detection.
• Vexcel Data: Famous for massive aerial ultra-high-resolution collection (including post-disaster "gray sky" emergency flights). Their Elements AI Damage Assessment tool automatically evaluates property-level destruction (calculating missing roof material, structural collapse, and tarp coverage in seconds).
• Capella Space: A leader in Synthetic Aperture Radar (SAR). Because SAR can "see" through clouds, darkness, and smoke, Capella’s automated analytics layers are exceptionally strong for rapid hazard and change detection during active natural disasters or covert tracking where optical imagery fails.

2. Dedicated GeoAI & Custom Model Builders

• Picterra: A self-serve geospatial machine learning platform. Instead of relying purely on out-of-the-box models, Picterra allows your team to upload training data and custom-train your own AI detectors for unique objects, local infrastructure hazards, or specific environmental degradation without writing heavy code.
• SpaceKnow: Offers scalable macro-to-micro analytics platforms that process broad national areas using AI. They excel at automated tracking of economic indicators, construction progress, and industrial/environmental anomalies or hazards at scale.
• Planet Labs: While historically known for global daily monitoring via optical constellations, Planet has pushed hard into edge-computing and on-orbit AI (running live object-detection models directly on hardware in orbit), drastically reducing the latency of getting alerts from space.

3. Enterprise GIS & Workflow Integration

• Esri (ArcGIS Pro / GeoAI): If you already have an internal GIS team, Esri supports deep learning and pre-trained GeoAI models directly inside ArcGIS. They publish open-access building and disaster damage assessment models (used heavily in wildfire and hurricane recovery) that you can run against satellite or drone feeds and instantly push into operational dashboards.

To help narrow down which vendor fits best, tell me:

• What kind of imagery are you using (optical satellite, SAR, or low-altitude drone/aerial)?
• Do you need out-of-the-box classification or the ability to train custom models for proprietary objects?

Yes. The strongest vendor set depends heavily on whether you mean satellite/aerial imagery, drone imagery, or street-level/ground imagery, and whether you want an off-the-shelf detector or a platform where you train your own models.

For object, damage, and hazard classification from geospatial imagery, I’d put these on a serious evaluation list:

How I'd segment the market

If your core problem is disaster damage: Look closely at Nearmap, PNNL/RADR, and the Microsoft HASTE/xView2 ecosystem. xView2 specifically established a useful benchmark around building-damage classification across disasters such as fires, floods, earthquakes and storms.

If you're looking for tiny/hard-to-detect hazards: Safe Pro Group is unusually specialized. Its focus on drone imagery and objects such as mines, UXO and other small threats makes it quite different from generic "geospatial AI" platforms.

If you need broad GEOINT/ISR object detection: BlackSky is worth evaluating, particularly if you also need the imagery supply chain and near-real-time satellite intelligence rather than merely an inference API. Its current AI roadmap explicitly includes automated target recognition and battle-damage detection.

If you want multimodal physical-risk analytics: OmniGeo is an interesting newer approach because it isn't limited to RGB imagery—it explicitly combines optical, multispectral, radar, LiDAR and thermal data.

One important distinction

I'd separate vendors into three architectures:

1. Imagery + proprietary AI — e.g. BlackSky, Nearmap. You buy both the data and the intelligence layer.
2. Specialized detection product — e.g. Safe Pro. You have a defined object/hazard taxonomy and want the vendor's models to do the detection.
3. Bring-your-own-imagery / train-your-own-model platform — the more interesting category if you have your own taxonomy of objects, damage states, or hazards. Here the key evaluation criteria are model adaptation, labeling, geospatial inference, temporal change detection, uncertainty estimates, and API/edge deployment—not simply model accuracy on a vendor demo.

If you're building a product rather than buying an intelligence feed, I'd focus the competitive evaluation on model customization, training-data requirements, inference API, sensor/modalities supported, spatial resolution, temporal change detection, and whether predictions come with confidence/uncertainty and polygons rather than just image labels. Those factors will matter considerably more than a generic "AI accuracy" number.

How I'd segment the market

If your core problem is disaster damage: Look closely at Nearmap, PNNL/RADR, and the Microsoft HASTE/xView2 ecosystem. xView2 specifically established a useful benchmark around building-damage classification across disasters such as fires, floods, earthquakes and storms.

If you're looking for tiny/hard-to-detect hazards: Safe Pro Group is unusually specialized. Its focus on drone imagery and objects such as mines, UXO and other small threats makes it quite different from generic "geospatial AI" platforms.

If you need broad GEOINT/ISR object detection: BlackSky is worth evaluating, particularly if you also need the imagery supply chain and near-real-time satellite intelligence rather than merely an inference API. Its current AI roadmap explicitly includes automated target recognition and battle-damage detection.

If you want multimodal physical-risk analytics: OmniGeo is an interesting newer approach because it isn't limited to RGB imagery—it explicitly combines optical, multispectral, radar, LiDAR and thermal data.

One important distinction

I'd separate vendors into three architectures:

When building or deploying AI to classify objects, structural damage, or environmental hazards from geospatial imagery, the vendor landscape splits into data-plus-analytics providers, dedicated computer vision / AI platforms , and enterprise GIS frameworks.

The strongest vendors in the space depend on whether you need a vertically integrated data feed or a model-building ecosystem:

• Planet Labs — Known for its massive daily satellite constellation. Planet Analytics leverages machine learning and AI to automatically detect and classify objects (vehicles, vessels, buildings, roads) and run large-scale change detection. Excellent for frequent monitoring and macro-level tracking.
• Maxar Technologies — A dominant force in very high-resolution (VHR) optical satellite imagery and geospatial intelligence. Maxar’s advanced analytics and foundation models are widely used for precise object identification, infrastructure mapping, and detailed pre- and post-disaster structural assessments.
• BlackSky — Offers real-time global monitoring and rapid-revisit tasking. Its Spectra AI platform uses AI automation to process high-cadence imagery and third-party sensors, flagging suspicious activities, tactical changes, or site anomalies in near-real-time.
• Vexcel Data Imaging — Renowned for hyper-clear aerial capture, their Vexcel AI Damage Assessment tool automatically calculates property-level metrics after catastrophic weather (such as missing roof segments, structural collapse, or tarp coverage) in seconds.
• Esri (ArcGIS) — The industry standard for enterprise GIS. ArcGIS Pro integrates deep learning tools directly with computer vision frameworks and cloud APIs (like Azure OpenAI vision models or custom PyTorch/TensorFlow weights). It is ideal if you already own spatial data infrastructure and want to run inference, object classification, and damage workflows in-house.
• ICEYE — Specializes in Synthetic Aperture Radar (SAR) constellations. Unlike optical providers, ICEYE can "see" through clouds, smoke, and total darkness, making it uniquely powerful for automated hazard and flood-extent mapping or night-time disaster assessment.

To help narrow down which vendor fits your project, let me know:

• Are you working with optical (RGB/multispectral) imagery or radar (SAR) data?
• Is your primary use case real-time disaster response, insurance claims , or infrastructure monitoring?

For AI classification of objects, damage, and hazards from geospatial imagery, the strongest vendors depend heavily on whether you need prebuilt intelligence, custom model training, satellite-scale analytics, drone/aerial inspection, or defense-grade object detection. The market is fragmented: some vendors excel at imagery + analytics, others at AI models, and others at operational workflows.

A shortlist by capability:

Best fits by mission

1. Disaster damage assessment (buildings, roads, floods, fires)

Strong candidates:

• Nearmap — very strong for property-level damage classification and insurance/emergency workflows.
• ICEYE — excellent when clouds, smoke, or night conditions make optical imagery unreliable.
• Maxar Intelligence — high-resolution imagery plus analyst workflows.
• FlyPix AI — useful if you want to train/customize models on your own imagery.
• Terrafort — focused specifically on AI-assisted disaster resilience and rapid damage assessment workflows.

2. Generic object detection (vehicles, buildings, equipment, infrastructure)

Look at:

• Picterra — good for creating custom detectors without building a full ML stack.
• FlyPix AI — supports user-trained geospatial models across satellite, aerial, and drone data.
• Maxar Intelligence — strong when paired with analyst workflows and very high-resolution imagery.
• BlackSky — good for persistent monitoring and change alerts.

3. Hazards (floods, fires, landslides, environmental risks)

Strong options:

• ICEYE — SAR-based flood and disaster monitoring.
• Planet Labs — frequent revisit monitoring.
• Capella Space — SAR analytics.
• Orbital Insight — large-scale analytics.
• Kayrros — energy/environment monitoring and remote sensing analytics.

4. Defense / intelligence-style imagery understanding

Often evaluated:

• Maxar Intelligence
• BlackSky
• Planet Labs
• Palantir Technologies (more of a data fusion/operations layer than an imagery vendor)
• HawkEye 360 (RF geospatial intelligence rather than imagery)

If you are building your own AI pipeline

The vendor choice changes. You may want:

• Imagery providers: Maxar, Planet, Airbus, ICEYE, Capella, Nearmap, Vexcel
• GeoAI platforms: FlyPix, Picterra, ArcGIS AI capabilities
• Cloud-scale ML infrastructure: AWS, Google Cloud, Microsoft Azure
• Foundation models / research tooling: open geospatial vision models plus your labeled data

My practical shortlist for an enterprise evaluation

If I were running an RFP for "AI that detects objects, damage, and hazards from geospatial imagery," I would likely include:

1. Maxar Intelligence — best high-resolution imagery + intelligence workflows
2. Nearmap — best for property damage and insurance/emergency response
3. ICEYE — best SAR capability for all-weather hazard monitoring
4. Planet Labs — best revisit frequency and global monitoring
5. FlyPix AI or Picterra — best for custom object classes and rapid model development
6. BlackSky — best for persistent monitoring/alerting

The key discriminator is usually not the neural network anymore; it is:

• access to the right imagery,
• labeled training data,
• ability to explain detections,
• integration with GIS systems,
• and operational workflows after the model finds something.

If you share the target domain (e.g., wildfire, military ISR, insurance claims, utilities, oil & gas, construction, agriculture, or autonomous systems) and whether you have your own imagery, the vendor shortlist would narrow considerably.

Yes. The market is fairly fragmented, and the “strongest” vendor depends heavily on whether you mean generic object detection, post-disaster damage, or hazard detection from satellite/aerial/drone imagery.

My shortlist

If you're building a vendor landscape

I'd divide the market into four tiers rather than treating all of these as competitors:

1. Imagery + proprietary AI

• BlackSky
• Nearmap
• Planet
• Maxar/Vantor
• EagleView

2. GeoAI platforms where you bring imagery

• FlyPix AI
• Picterra
• NV5/ENVI
• Hexagon/ERDAS
• Esri

3. Vertical AI applications

• Overstory → vegetation/utilities
• ZestyAI → property/insurance
• Nearmap → property/disaster
• Safe Pro → hazards/defense
• TerraFort → disaster response

4. Custom/model-building infrastructure

• Microsoft/AI for Good
• Google Earth Engine
• NVIDIA/edge CV ecosystem
• Open-source computer-vision models

The particularly interesting distinction

If your requirement is literally:

“Give us geospatial imagery and let us detect/classify arbitrary objects, damage, or hazards.” I'd put FlyPix AI / Picterra-type platforms near the top of the evaluation list, because they're closer to a general-purpose geospatial AI layer.

If instead you want:

“We know the object/damage category and need extremely reliable production detection.” I'd favor vertical specialists. For example, Nearmap for property damage, Overstory for vegetation hazards, and Safe Pro for small hazardous objects.

And if you need defense/ISR-grade object detection and rapid satellite tasking, BlackSky becomes a much more serious candidate.

One other important point: damage detection is still substantially harder than ordinary object detection. A 2026 operational study notes that disaster-response UAS imagery can arrive at enormous volumes, and even operational AI damage assessment remains an emerging state of practice. AAAI Publications Microsoft's HASTE work is also notable because it tackles the practical problem of generating event-specific building-damage models when suitable before/after training data aren't available.

If you're evaluating vendors for a product/platform decision, I can also build you a 15–20 company competitive matrix covering imagery source, satellite/aerial/drone, object detection, segmentation, damage classification, custom model training, API availability, inference latency, GIS integration, and likely pricing/enterprise maturity.

When using AI to classify objects, structural damage, or environmental hazards from geospatial imagery (satellite, aerial, or drone), vendors generally fall into three categories: End-to-End Enterprise GIS Platforms, Constellation & Data Providers with Built-in Analytics , and Specialized Computer Vision / Disaster Intelligence Feeds.

The strongest vendors and platforms in the space are categorized below by their primary strengths:

1. Enterprise GIS & GeoAI Platforms (Best for Custom Modeling & Integration)

• Esri ArcGIS: The dominant ecosystem for spatial analysis. Through ArcGIS Pro and ArcGIS Image, Esri provides robust deep learning and GeoAI toolsets. It natively supports integration with TensorFlow, PyTorch, and Hugging Face models, allowing you to train custom object-detection or damage-assessment models using your own labeled drone, aerial, or satellite inputs.
• QGIS (with Deepness Plugin): For an open-source route, QGIS combined with community plugins like Deepness lets users run object detection and segmentation models directly on orthophotos and raster layers without building a massive external processing pipeline.

2. Satellite & Aerial Data Providers with Native AI Analytics

• Maxar Technologies: A premier force in high-resolution commercial satellite imagery. Maxar partners tightly with enterprise GIS ecosystems (like Esri) and applies advanced computer vision workflows to derive automated damage assessments, change detection, and object classification directly from its massive imagery archives.
• Planet Labs: Known for high-frequency, global daily Earth observation. Planet’s analytics pipelines use machine learning to automatically flag changes in land cover, infrastructure development, and environmental alterations at scale over time.
• Vexcel Imaging (Elements AI Damage Assessment): If you are working specifically with high-resolution aerial disaster/catastrophe imagery, Vexcel provides automated property-level insights (calculating missing roof material, structural collapse scores, and tarp coverage) in seconds via applied machine learning.

3. Specialized Catastrophe & Hazard Intelligence

• Nearmap (Disaster Insights): Highly specialized in post-catastrophe aerial capture paired with instant, AI-driven damage classification (identifying structural damage, temporary roof repairs, and vegetation debris) at a granular, property-by-property level.

If you'd like to narrow this down, tell me:

• What is the source of your imagery (satellites like Planet/Maxar, or low-altitude drones/aircraft)?
• Are you looking for a turnkey SaaS API that hands you the classification results, or a desktop tool/framework to train your own custom models?

For AI classification of objects, damage, and hazards from geospatial imagery (satellite, aerial, drone, SAR, LiDAR, street-level), the strongest vendors depend heavily on whether you need off-the-shelf analytics, custom model training, or mission-specific geospatial intelligence.

A useful way to segment the market:

For specific missions

Disaster damage assessment (buildings, fires, floods, storms)

Strong candidates:

1. Esri + custom models
2. Nearmap for property-level aerial damage
3. FlyPix AI for rapid deployment
4. BlackSky for satellite-based monitoring

Damage assessment is a particularly mature AI application: models can detect buildings, compare pre/post imagery, and classify severity levels. The xView2 benchmark and related efforts have driven much of this capability for satellite-based disaster response.

Defense / intelligence / surveillance

Look at:

• BlackSky
• Maxar Intelligence
• Planet Labs
• Palantir Technologies

Key capabilities:

• vehicle/vessel/aircraft detection
• activity change detection
• infrastructure monitoring
• multi-source fusion (imagery + other intelligence)

Custom object detection (e.g., "find abandoned vehicles," "detect damaged poles," "identify hazards)

Look at:

• Esri
• Picterra
• FlyPix AI
• custom computer vision vendors using models such as YOLO, Detectron, SAM, and vision-language models

Critical infrastructure monitoring

Look at:

• Vexcel Imaging
• Maxar Intelligence
• BlackSky
• Planet Labs

What I would shortlist for an enterprise evaluation

If you need a production platform quickly:

• Esri
• Nearmap
• FlyPix AI
• BlackSky

If you need to build proprietary AI models:

• Esri
• Picterra
• FlyPix AI
• cloud ML stacks (AWS SageMaker, Azure ML, Google Vertex AI) with imagery pipelines

If you need government/defense-grade GEOINT:

• BlackSky
• Maxar
• Planet
• Palantir
• Esri

The biggest evaluation criteria are usually:

• imagery resolution (10 m vs 30 cm makes a huge difference)
• sensor types (RGB, multispectral, SAR, LiDAR)
• ability to train on your labels
• geospatial accuracy (polygon quality, not just classification accuracy)
• explainability/audit trails
• latency from image capture to answer

If you share the target domain (e.g., wildfire, military ISR, insurance claims, infrastructure inspection, agriculture, maritime, or urban hazards) and the imagery source (satellite/drone/aerial/SAR), I can narrow this to a more realistic vendor shortlist.

For classifying objects, infrastructure damage, and environmental hazards from geospatial imagery, leading vendors include Satelytics, Nearmap, Vexcel Imaging, and Maxar Technologies , alongside enterprise GIS platforms like Esri.

Selecting the right vendor depends on whether your focus is on infrastructure risk monitoring, post-disaster structural damage, or defense-grade object tracking.

Top Geospatial AI Vendors & Platforms

Key Selection Criteria

• Resolution and Sensor Type: Satellite providers (like Maxar) offer global scale but lower resolution per pixel compared to aerial survey vendors (like Nearmap or Vexcel), which are required for granular roof damage or minor hazard classification.
• Turnkey vs. Custom Models: Platforms like Satelytics provide turnkey alerts for specific industrial hazards, whereas Esri ArcGIS is optimized if you intend to train and deploy your own custom PyTorch/YOLO-based computer vision models on proprietary data.
• Temporal Frequency: Rapid post-event response requires vendors with dedicated disaster-response aircraft or responsive satellite tasking agreements.

If you want to narrow this down, tell me:

• What type of imagery are you using (satellite, manned aerial, or drone)?
• Are you looking for a turnkey SaaS platform or tools to build custom AI models?

I can provide a more tailored vendor recommendation or technical architecture.

Classifying objects, damage, and hazards from geospatial imagery relies on a mix of enterprise GIS platforms, high-frequency satellite analytics , and custom AI builder tools.

The strongest vendors in the geospatial AI ecosystem are categorized below by their primary strengths:

1. Enterprise GIS & Heavy-Duty Processing

• Esri: The industry heavyweight. Through ArcGIS Pro with AI , Esri allows GIS professionals to train deep learning models, run pre-built object/damage detection tools, and integrate smoothly with enterprise databases. Best if you already have an enterprise GIS backbone.
• Google Earth Engine: Unmatched for planetary-scale raster processing. While it requires more coding (Python/JavaScript), it provides massive cloud computing capacity to run machine learning models over multi-decadal satellite data stacks.

2. High-Frequency Monitoring, Change Detection & Disasters

• BlackSky: Excellent for time-sensitive applications like defense, crisis response, and rapid hazard monitoring. Their Spectra AI platform specializes in high-revisit monitoring, detecting micro-changes or object movements almost immediately after image capture.
• Planet: Known for daily global coverage. Planet applies AI to its massive satellite constellations to generate derivative products (like canopy height, infrastructure changes, and wildfire fuel/vegetation hazard mapping).
• Vexcel Data: Particularly strong in post-disaster insurance and property analytics. Their AI-driven Damage Assessment tools process high-resolution aerial disaster imagery to calculate roof destruction, structural severity, and tarp coverage within seconds.

3. Custom Object Detection & Point-and-Click AI

• Picterra: A brilliant option if you want a custom, user-friendly geospatial AI platform without heavy data science overhead. It features a point-and-click interface allowing commercial drone or satellite operators to train custom object and pattern recognition models.
• Orbital Insight: Specializes in multi-class object detection algorithms (identifying ships, aircraft, vehicles, and macro-infrastructure changes) by fusing multiple geospatial data sources into their GO platform.
• Satelytics: Niche-focused and powerful for infrastructure hazards. They use cloud-based AI on high-res commercial imagery to automatically detect environmental leaks, ground movement, and vegetation encroachment on utility/pipeline corridors.

To help narrow down which vendor fits your project, tell me:

• What kind of imagery resolution are you using (satellite, high-res aerial, or drone)?
• Are you looking for a turnkey SaaS platform or a developer toolkit/API to build your own models?

Yes. If the requirement is AI that turns satellite/aerial/drone imagery into classifications of objects, damage, or hazards, I’d separate the market into platform vendors (you bring imagery and train/customize models) and vertical/imagery vendors (they provide both imagery and prebuilt AI).

Strong vendors to evaluate

My ranking by use case

"Detect anything we define from our imagery" → Picterra

This is the most direct match if you have your own imagery and want to build models for things such as:

• vehicles, buildings, aircraft, ships
• road defects
• illegal dumping
• vegetation encroachment
• construction activity
• flood/wildfire indicators
• utility infrastructure
• unexploded/abandoned structures
• before/after change

Picterra explicitly supports custom detectors and production deployment rather than restricting you to a predefined catalog.

"Tell me what's damaged after a disaster" → Nearmap or Vexcel

Nearmap has a particularly mature property-damage workflow: its current model uses a five-level damage classification aligned to FEMA guidance, with models trained across dozens of catastrophe events.

Vexcel is compelling if you also want its high-resolution aerial/Gray Sky disaster imagery and broader property/infrastructure intelligence. Its Damage Assessment product produces property-level damage scores and damage footprints.

"We have an enormous satellite archive and need scalable geospatial ML" → Descartes Labs / Picterra

"Real-time or near-real-time aerial ISR / object-of-interest detection" → Overwatch Imaging

The important architectural question

I'd actually avoid choosing purely on model accuracy. Ask each vendor whether they support this pipeline:

Imagery → preprocessing → detection/segmentation → confidence score → geospatial geometry → temporal change detection → API → your GIS/data platform

In particular, determine whether they can ingest your imagery, rather than forcing you to purchase their imagery. That's the biggest dividing line between something like Picterra and Nearmap/Vexcel.

For a serious enterprise evaluation, I'd run the same 500–2,000 labeled scenes through 3–4 vendors and compare:

1. Precision / recall by object class
2. Small-object detection
3. False positives per km²
4. Performance across different sensors/resolutions
5. Cloud/smoke/night robustness
6. Polygon vs. bounding-box quality
7. Change detection accuracy
8. Inference latency
9. API/batch-processing throughput
10. Ability to retrain with your labels
11. Data residency / security
12. Cost per km² or image

If you tell me what the imagery is (satellite, aerial, drone, SAR, etc.), the objects/hazards you're detecting, and roughly how many km²/images per day, I can narrow this to a 5-vendor shortlist and compare capabilities, APIs, pricing model, and likely accuracy.

Vexcel is compelling if you also want its high-resolution aerial/Gray Sky disaster imagery and broader property/infrastructure intelligence. Its Damage Assessment product produces property-level damage scores and damage footprints.

"We have an enormous satellite archive and need scalable geospatial ML" → Descartes Labs / Picterra

"Real-time or near-real-time aerial ISR / object-of-interest detection" → Overwatch Imaging

The important architectural question

I'd actually avoid choosing purely on model accuracy. Ask each vendor whether they support this pipeline:

Imagery → preprocessing → detection/segmentation → confidence score → geospatial geometry → temporal change detection → API → your GIS/data platform

In particular, determine whether they can ingest your imagery, rather than forcing you to purchase their imagery. That's the biggest dividing line between something like Picterra and Nearmap/Vexcel.

For a serious enterprise evaluation, I'd run the same 500–2,000 labeled scenes through 3–4 vendors and compare:

1. Precision / recall by object class
2. Small-object detection
3. False positives per km²
4. Performance across different sensors/resolutions
5. Cloud/smoke/night robustness
6. Polygon vs. bounding-box quality
7. Change detection accuracy
8. Inference latency
9. API/batch-processing throughput
10. Ability to retrain with your labels
11. Data residency / security
12. Cost per km² or image

If you tell me what the imagery is (satellite, aerial, drone, SAR, etc.), the objects/hazards you're detecting, and roughly how many km²/images per day, I can narrow this to a 5-vendor shortlist and compare capabilities, APIs, pricing model, and likely accuracy.