Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision
arXiv:2606.20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes. Wall-to-wall, annually updated maps are a persistent need for effective forest management. Many planning systems and data collections combine disparate data sources with different purposes, vintages, and prediction quality, which leads to confounding behavior in operational planning systems. We introduce the VibrantForests framework, developed and applied to map forest attributes and provide a coherent foundati
The increasing availability of diverse remote sensing data (Lidar, satellite imagery) and advancements in computer vision are converging to enable more sophisticated and wall-to-wall environmental mapping solutions.
This development allows for improved, actionable science for forest and wildfire risk management, crucial for environmental sustainability, resource allocation, and disaster mitigation.
The ability to generate coherent, annually updated forest structure maps from disparate data sources significantly enhances the quality and reliability of operational planning systems in forestry.
- · Forest management agencies
- · Environmental monitoring companies
- · Remote sensing technology providers
- · AI/Computer Vision developers
- · Manual survey methods
More accurate and timely data for carbon sequestration accounting and biodiversity monitoring becomes available.
Enhanced forest health data informs policy decisions impacting timber industries, conservation efforts, and climate change strategies.
The success of this framework could inspire similar multi-sensor data integration approaches for other critical environmental sectors, driving broader adoption of applied AI in ecological governance.
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Read at arXiv cs.LG