Environmental Management
AI is increasingly being used to support land and environmental management, particularly in assessing and reducing impacts on biodiversity. These tools can process large and complex datasets to detect patterns, track ecological change, and automate reporting.
AI models analyse data from sources like satellite imagery, camera traps, bioacoustics sensors, and environmental DNA (eDNA). This helps organisations monitor species, understand habitat health, and identify biodiversity risks—tasks that would otherwise be time-consuming and costly.
For example, some systems use AI to combine biodiversity data from multiple sources and provide insights tailored to the infrastructure sector. This includes automated reports aligned with frameworks like Biodiversity Net Gain and the Taskforce on Nature-related Financial Disclosures (TNFD).
Surveyors working in land and infrastructure projects can use AI-generated biodiversity data to:
- Understand the ecological value of a site before development begins
- Identify sensitive areas that need protection or enhancement
- Inform site selection, design changes, or mitigation strategies
- Support planning applications with evidence-based environmental reporting
- Monitor biodiversity impacts over time and ensure compliance with policy or legal requirements
While AI offers clear benefits, there are also risks. These include poor data quality, over-reliance on automated outputs, and misinterpretation of insights without expert input. In some cases, lack of transparency about how AI models work or where the data comes from can also reduce trust in the outcomes.
To mitigate these risks, AI should be treated as a support tool—not a replacement for professional judgement. Surveyors should combine AI outputs with on-the-ground observations and ecological expertise. Using reliable, context-specific data and maintaining clear documentation of how AI insights inform decisions is essential for responsible use.
Principal author: Aaliyah Pollock