- Published date:
- 25 September 2024
Leveraging AI for Sustainable Development and Biodiversity Net Gain (BNG)
By Shashin Mishra,
VP of EMEA at AiDash
As we enter an era where environmental challenges intensify, cutting-edge solutions like artificial intelligence (AI) are reshaping how we address these issues. AI offers immense potential in driving sustainable development, particularly in achieving Biodiversity Net Gain (BNG) targets - where developments must leave the environment in a better state than before.
Given the scale of the biodiversity net gain that needs to be achieved, AI is going to be a crucial ally to policymakers. By harnessing vast amounts of remote sensing data, satellite imagery, and deploying sophisticated algorithms, AI can provide unprecedented insights into habitat health, biodiversity assessments, and ecosystem restoration strategies.
Understanding Biodiversity Net Gain (BNG)
As the UK continues to implement Biodiversity Net Gain (BNG) as a key policy under the Environment Act 2021, developers and landowners are facing the challenge of integrating environmental priorities into development projects. The goal of BNG is to ensure that new developments leave the natural environment in a measurably better state than it was before the project began. While BNG is critical for reversing biodiversity loss, the complexity of planning, assessing, and monitoring biodiversity outcomes often presents significant challenges to developers, ecologists, and local planning authorities (LPAs).
The Role of AI in Achieving BNG
AI has already demonstrated its value across various environmental domains, and its application to BNG is becoming increasingly apparent. From data collection to monitoring and compliance, AI can revolutionise the key stages of the BNG process, making it more efficient and precise.
1.AI-Driven Data Collection
A fundamental aspect of achieving BNG is conducting accurate baseline biodiversity assessments of development sites. These assessments determine the existing quality and quantity of habitats, which form the basis for calculating biodiversity units under tools like the Defra Statutory Biodiversity Metric.
Traditional surveys often require significant time and effort from teams of ecologists who manually collect data across large or inaccessible areas. This process can be slow, labour-intensive, costly, and subject to human error. AI technologies, however, are transforming this space by improving the speed, accuracy, and scope of data collection.
- Harnessing Remote Sensing and Satellite Data for BNG through AI
- Habitat Mapping and Classification
- Recent Dated Image - Utilizing imagery with a clear and recent date stamp is crucial. This ensures the data reflects the current state of the habitat, making it relevant for analysis and decision-making.
- High Resolution - High-resolution imagery is non-negotiable for detailed habitat mapping. This level of detail allows for precise delineation of habitat boundaries and identification of key features, minimizing errors and enhancing the overall quality of the assessment.
- Positionally accurate - Positionally accurate imagery is essential for precise habitat mapping and effective Biodiversity Net Gain (BNG) strategies. It ensures that habitat boundaries are mapped correctly, minimizing errors and overlaps.
- Condition Assessments: Rivers, Habitats, and Ecosystems
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