Case Study

AiDash climate tech to explore biodiversity condition assessment


UK government and nonprofits support focused effort to meet biodiversity mandates


Company: Agriculture and farming organization 
Location: United Kingdom 
Solution: AiDash Intelligent Sustainability Management System (ISMS) 
Methods: DEFRA (UK) – Natural England, BNG V3.1 as baseline method 



Sample scale: remotely assess condition criteria for ecological surveys  
Value proposition: reduce time for professional ecology surveys and measurements  
Accuracy: hybrid approach with manual surveyance, optimizing field data assessments 
Validation confidence: machine learning models compared against field assessments 
Applicability: grassland, woodlands, shrubland, wetland, heathland
broad habitats   



Remote assessment of habitat condition for biodiversity V3.1 standards 

Working with agricultural partners and ecology specialists through a U.K. government sponsored study, AiDash is currently assessing the viability of using Earth observation and other remote sensing data to accelerate ecological field studies. Specifically, the study aims to apply accurate methods for assessing habitat condition criteria.  

 The initial work stage focuses on 5 broad habitats that account for the majority of U.K. land, with a view to extend to all habitats in time. Combined, the 5 broad habitats comprise 50 criteria used to judge condition.   

The U.K. government’s Environment Act 2021 requires a 10% biodiversity net gain (BNG) for developments as part of the government’s wildlife and biodiversity restoration strategy. The U.K. approach to assessing biodiversity includes a combined assessment of the land habitat type (or classification), condition of each habitat, and strategic importance. In Spring 2022, the U.K. government introduced a set of standard criteria to drive consistent condition assessments for 25 broad habitats (covering all land and coastal terrain and all sub-level habitat classes).  

AiDash is working with a regenerative farming partner with land spanning 2,000 acres to test the models by mid 2023. An independent ecology specialist will support the study and evaluate the validity of the model outputs.  



New features to support biodiversity assessments and speed up field surveys 

In most biodiversity assessments, professional ecologists review conditions manually. Large areas with complex ecosystems, therefore, can require increased time and cost for specialized surveyance. However, technology-driven solutions can augment field surveys with pre-processed additional datasets — enabling inspection surveys to focus on habitat areas and criteria questions that require manual assessment.  

AiDash’s Intelligent Sustainability Management System (ISMS Biodiversity), assesses the classification of habitats and provides a platform to monitor, plan, and disclose biodiversity. New features expand the satellite-based approach to accelerate and support the optimization of in-depth professional ecology surveys. Selected condition criteria that require very-high-resolution images on the ground may also be assessed using photographs, which may be collected and processed prior to the ecologist site inspection — further refining where professional judgement is needed.        



Hybrid: human, EO* image, AI, and existing dataset retrieval to aid ecologists and their client organizations 

AiDash’s ISMS climate technology, launched at COP26, uses commercial grade 10cm-50cm pixel aerial-satellite imagery with AI. Combining this data with public or enterprise records and validation by in-house ecologists, AiDash produces habitat assessments to standard ecological minimum mapping units (that is, 5m by 5m). Large or distributed estates can be assessed quickly, enabling land managers, developers, and ecologists to optimize plans and efficiently monitor land enhancements between surveys.  

ISMS also: 

  • Measures biodiversity change over time (annual or periodic, historic evaluation). 
  • Provides data via a GIS-based platform designed to support organizational record retention, site planning, and project governance, data sharing between project partners, and site- or portfolio-wide reporting.  
  • Can be accessed via cloud, and acts as a client-controlled nature-reporting land management system. 
  • Allows separate ISMS Carbon and ISMS Natural Capital solutions to be combined for in-depth carbon assessments or broader land sustainability tracking.  

The project includes standardized condition survey questions to evaluate grassland, heathland, shrubland, wetland and woodland areas (cropland is excluded from condition assessments). The objective is to develop models for
~ 70% of the condition criteria questions. 

If the approach proves viable, then all habitat condition criteria (lakes, urban, rivers, and so forth) will be added and developed in time. A combination of sensors is being assessed, including digital surface modelling (DSM), synthetic aperture radar (SAR), photographs, multispectral imagery, and near-infrared imagery. Other datasets may be evaluated as they become commercially available**.  

*Earth observation
**Thermal, hyperspectral, and so forth. 



About AiDash  

AiDash is an AI-first vertical SaaS company on a mission to transform operations, maintenance, and sustainability in industries with geographically distributed assets by using satellites and AI at scale. With access to a continual, near-real-time stream of critical data, utilities, energy, mining, and other core industries can make more informed decisions and build optimized long-term plans, all while reducing costs, improving reliability, and achieving sustainability goals. To learn more about how AiDash is helping core industries become more resilient, efficient, and sustainable, visit