What is AI in natural resource management? AI in natural resource management refers to the use of satellite imaging, computer vision, and predictive algorithms to monitor ecosystems, optimise resource extraction, and support sustainable conservation efforts. The scope of these technologies encompasses automated land cover mapping, predictive water flow modelling, environmental impact assessments, and real-time wildlife tracking. Key deliverables include forest density analyses, predictive agricultural yield forecasts, and automated carbon offset verification reports. Organisations and government bodies deploy these systems when managing complex climate risks, accelerating ecological restoration, and ensuring compliance with regulatory bodies like the EA and DEFRA. Brainpool enables mid-sized environmental consultancies and natural resource enterprises to scale these capabilities using our Cortex platform. Drawing on our extensive professional services experience, we build bespoke spatial and predictive models that turn environmental data into sustainable, compliant actions.
There is a need for green technology to mitigate and reverse environmental challenges that humanity will continue to face in the coming decades. Governments are taking steps to implement smarter data analytics systems to manage natural resources in a sustainable way.
Thanks to IoT, the data production capability of forestry has reached new heights. Brainpool is working with Governmental Institutions to ensure that AI is being leveraged in the green transformation. New technologies provide exciting opportunities to re-think how humans interact with the natural world and provides the tools to better monitor and manage natural resources to meet the needs of industry and society, now and in the future.
Satellite images can be analysed using machine vision to monitor natural resource quality and usage, informing decisions about resource management and sustainability policies and initiatives.
Benefits:
Efficient resource management
Smarter decisions leading to increased sustainability
Data from remote sensors can be combined with predictive analytics to determine which areas of farmland will emit the most greenhouse gases to adjust regulations, incentives or collaboration.
Benefits:
More effective emission legislation
Faster response times
Less CO2 emission
ML can predict the movement and spread of dangerous events such as fire, evaluate high-risk regions and help guide controlled cut and burn-back to prevent progression of fire.
Benefits:
Early warning systems
Less resource damage
Machine learning can be used to locate appropriate planting sites, monitor plant health, assess weeds, and analyse trends to facilitate smarter and more efficient agriculture.
Benefits:
Better harvests
Improved food quality
Our client aimed to develop a platform that would enable them to track changes in abundance of natural resources, conduct risk analysis and forecast resource demand under different government policy scenarios; enabling them to proactively take action to ensure accessibility and sustainability of resources across generations.
Brainpool developed a proof of concept (PoC) model that uses SOTA pre-trained deep-learning networks models for object detection and semantic segmentation to process satellite imagery (and remote sensing data) for a case study region of Canada for the purposes of creating a lower dimensional dataset suitable for use in an agent-based modelling environment. The model currently in development is designed to: (ii) Combine scientific, economic and geospatial data with lower dimensional dataset to estimate biomass value functions for various biomass types (agricultural, forest, and organic wastes). (iii) Simulate biomass dynamics under different supply & demand conditions, including responding to natural disturbance events in an agent-based modeling environment
Not available yet (project in progress).
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