The Future of Ecological Intelligence

Salt Marsh Intelligence Infrastructure
for the Next Century.

ecoVision combines UAV imaging, transformer-based computer vision, and ecological modelling to automate species-level vegetation monitoring across vulnerable ecosystems, grounded in peer-reviewed research at Keele University.

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ECOVISION — ANALYSIS PREVIEW
PREVIEW
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Species Confidence

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PIPELINE STATUS

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DOMINANCE INDEX
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The Global Problem

Ecological monitoring has not scaled with
environmental collapse.

01

Manual Surveys Can't Scale

  • Labour-intensive and expensive
  • Inconsistent methodology
  • Spatially limited coverage
  • Months to deliver results
02

Satellites Lack Precision

  • 10-30m resolution misses species
  • No temporal flexibility
  • No ecological granularity
  • Cloud interference limits coverage
03

Ecosystems Are Collapsing Faster

  • 30% global wetland loss since 1970
  • Pioneer zones shift in weeks
  • Monitoring lags biodiversity collapse
  • Decision-makers lack real-time data

The Platform

From drone imagery to
ecological intelligence.

A four-stage AI pipeline built for distinct ecological perception tasks.

01

UAV Acquisition

DJI Phantom 4 Pro · 30m AGL · 2cm GSD

Systematic grid flights over intertidal survey zones. 80% overlap enables photogrammetric reconstruction and raw RGB orthomosaics at 2.1cm/px.

Specifications

Altitude
30m AGL
Resolution
2.1 cm/px
Overlap
80% fwd / 75% side
Format
GeoTIFF ortho

Research Foundation

Built on peer-reviewed
ecological and AI research.

EcoVision 2.0 is grounded in reproducible science, GIS workflows, and model validation.

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Pixel accuracy · unverified
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Species classification · unverified
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Mean IoU · unverified
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Dominance MAE · unverified
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Transformer Vegetation Segmentation

Comparison content from the research script, preserved for later editorial selection.

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UAV Species Intelligence

Comparison content from the research script, preserved for later editorial selection.

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Dominance Mapping Framework

Comparison content from the research script, preserved for later editorial selection.

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Ecological AI Infrastructure

Comparison content from the research script, preserved for later editorial selection.

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Spatial Aggregation Systems

Comparison content from the research script, preserved for later editorial selection.

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MAPIE Conformal Prediction

Comparison content from the research script, preserved for later editorial selection.

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Pixel Accuracy
Not yet verified
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Species Classification
Not yet verified
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Dominance Error (MAE)
Not yet verified
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Ground Sampling Distance
Not yet verified
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Survey Speed vs. Manual
Not yet verified

Technology Stack

Institutional-grade
AI infrastructure.

Four architectural layers from edge delivery to research HPC.

Frontend Intelligence

  • Next.js · React
  • Cognito SSO
  • Presigned S3 Uploads
  • CloudFront CDN

AI Inference Layer

  • SegFormer-B5
  • ConvNeXt-Base
  • MAPIE Conformal
  • PyTorch · CUDA

Cloud Infrastructure

  • AWS ECS · Lambda
  • SQS · S3 buckets
  • EC2 GPU
  • CloudWatch

Research Compute

  • Keele HPC · SLURM
  • MLflow · DVC
  • VPN
  • TensorBoard

Roadmap

From saltmarsh monitoring to
ecological intelligence.

Active

British Saltmarsh

2024-25

Spartina maritima mapping
Foryd Bay validation
Dominance scoring v1

Developed in Partnership With

Keele University logo
AWS logo

Get Involved

Building the future of
ecological intelligence.

A new category of environmental infrastructure where AI, ecology, and scalable cloud systems converge.