SWAMP

What SWAMP does

SWAMP, as documented in the structured source material, is the entity behind Swamp-AI, a deep learning computer vision model designed to monitor and segment wetlands globally using Sentinel-2 satellite imagery. The model was developed with a 2019 Sentinel-2 imagery baseline and was formally introduced via a Nature journal publication dated 2026-02-13; in segmentation tests across diverse geographic locations it reportedly achieved high accuracy, suggesting technical viability for large-scale wetland change detection. SWAMP is described in the firmographic record as a private, for-profit entity headquartered in Madison, United States, with a headcount band of 11–50 employees; however, the firmographic header instead describes SWAMP as a "no-cost, open-source, high-performance computing platform for continuous software assurance," which contradicts the Swamp-AI characterization found in all source-tagged structured fields — this discrepancy is treated as a data quality issue rather than reconciled here.

The product surface consists of a single AI artifact (Swamp-AI) with no documented API, SDK, or MCP integration surface (api_exists: false), no certifications, no patents, no trademarks, no investments, no management team disclosure, and no marketing or distribution channels. The primary input modality is satellite imagery (Sentinel-2), and the AI capability type is computer vision; sub-brand Swamp-AI is explicitly registered as a DBA-equivalent in the source data. The company claims a global operating footprint.

The business model and monetization mechanics are not disclosed. There is no revenue figure, no pricing model, no select-customers list, no segmentation detail, no partnerships or alliances, and no funding rounds in the input. Consequently, the commercial posture is best characterized as research-to-publication with monetization yet to be defined; the discrepancy between the firmographic header (Software Assurance Marketplace) and the rest of the record indicates the structured SWAMP data is incomplete and any commercial conclusions should be treated as low-confidence.

Name

SWAMP

Legal name

SWAMP

Website

https://continuousassurance.org

Company type

Private

Operating status

Operating

Headcount range

11–50 employees

Short description

SWAMP is the entity behind Swamp-AI, a peer-reviewed deep learning model for global wetland segmentation and change detection trained on Sentinel-2 satellite imagery; the firmographic record lists it as a private for-profit entity in Madison, US, but no monetization, customers, or commercial GTM is disclosed.

Ownership category

akta.pro rank

Product category

Geospatial AI

NAICS

Geophysical Surveying and Mapping Services (541360)

akta.pro primary industry

Remote Sensing & Earth Observation Analytics (Satellite/UAV/LiDAR, Image Processing) (EUAHAKAD)

akta.pro secondary industries

Wetlands & Waters of the U.S. Delineation / Riparian Assessments (EUAHAHAC), 3D Vision, Depth Sensing & Point Cloud Analytics (HDAAAEAK)

Keywords

• Wetland monitoring
• Satellite imagery analysis
• Deep learning segmentation
• Geospatial AI
• Remote sensing analytics

Headquarters

HQ city

Madison

HQ country

United States

HQ region

North America

Markets served

GTM type

B2B

Offering type

Software

Cost components

Technology or R&D, Personnel, Infrastructure, Operations

Core offering

SWAMP offers Swamp-AI, a deep learning model designed to monitor wetlands globally using Sentinel-2 satellite imagery. The model performs image segmentation to detect and track wetland changes across diverse global locations, achieving high accuracy in segmentation tests.

Product overview

SWAMP offers Swamp-AI, a deep learning model for monitoring wetlands globally. This AI product processes satellite imagery to perform wetland segmentation and change detection.

Differentiator

Problem solved

Functional benefit

Brands

• Swamp-AI: Deep learning model for monitoring wetlands change across the globe using Sentinel-2 satellite imagery

Products and services

• Swamp-AI A deep learning model designed to monitor wetlands change across the globe using Sentinel-2 satellite imagery. It achieved high accuracy in segmentation tests across diverse global locations, demonstrating potential for large-scale wetland monitoring.

Ideal customer profiles1 record

Technology focussed Yes

API detail

Has API

No

API docs

API detail

Core technology

AI maturity

App detail

AI capability2 records

Feature1 record

Recent moves2 records

Expansion highlights3 records

Emerging players

• Pachama: Pachama uses satellite data and AI to measure and verify nature-based carbon projects including wetland and forest ecosystems. Comparable to SWAMP where wetland monitoring overlaps with carbon/nature MRV.
• SkyWatch: SkyWatch aggregates satellite imagery and provides APIs/platforms for downstream Earth observation analytics developers. Comparable to SWAMP as an enabler/peer in the satellite-imagery analytics ecosystem.

Direct peers

• Dendra Systems: Dendra Systems uses AI on aerial and satellite imagery to monitor ecosystem restoration and biodiversity outcomes. Directly comparable to SWAMP's use of deep learning for global habitat/wetland segmentation and restoration tracking.
• Satelytics: Satelytics applies AI to satellite imagery for environmental monitoring including water quality, land disturbance, and wetland/vegetation change. Direct peer in AI-driven satellite environmental monitoring.
• Descartes Labs: Descartes Labs builds AI on satellite imagery for agriculture, sustainability, and environmental monitoring. Comparable to SWAMP because both apply deep learning to geospatial data to deliver land-cover and change-detection analytics.
• EOS Data Analytics: EOSDA offers satellite imagery analytics with AI-powered land cover and vegetation monitoring for agriculture and environmental applications. Comparable to SWAMP in using deep learning on satellite data to map natural landscapes.

Broad incumbents

• Planet Labs: Planet operates a large commercial satellite constellation and offers imagery plus analytics for environmental and land-use monitoring. A broad incumbent adjacent to SWAMP's satellite-based wetland monitoring approach.
• Maxar Intelligence: Maxar provides high-resolution Earth observation imagery and analytics for government and commercial environmental and infrastructure monitoring. A broad incumbent in the satellite-based environmental analytics space SWAMP targets.
• Orbital Insight: Orbital Insight applies computer vision and AI to satellite and other geospatial data for environmental, energy, and economic monitoring. Comparable technology stack and target use cases to SWAMP's Swamp-AI.
• Microsoft AI for Earth (now part of Microsoft Planetary Computer): Microsoft Planetary Computer hosts petabytes of Earth observation data and AI-ready APIs for environmental research including land cover and wetland analytics. A broad incumbent whose free platform competes with SWAMP's open approach.

Market position

Strengths4 records

Weaknesses4 records

Competitive moat3 records

Key highlights5 records

Customer concentration

• Xx.com/SWAMPTEAM
• Facebookfacebook.com/SoftwareAssuranceMarketplace

Revenue estimate

Valuation estimate

Number of profiles

Funding overview

Funding rounds

Investors

Funding detail is available on the Subscription and Enterprise plan.Contact sales →

M&A

Investments

M&A and investment is available on the Subscription and Enterprise plan.Contact sales →

Frequently asked questions about SWAMP