In this article, we discuss the growing complexity and importance of geospatial programming and machine learning in the carbon market. As scrutiny over carbon credits increases, particularly in terms of land tenure and carbon issuance methodologies, there is a growing need for more rigorous mapping and carbon calculation methods.
As Geospatial developers, we've spent an enormous amount of time to develop a deep understanding of the latest carbon methodologies and helping our clients understand their credit issuance/revenues, and ultimately taking their projects to audit.
Here are some of our latest observations:
1. Increasing Complexity of Geospatial Data and Models
Geospatial data and machine learning applications in carbon management are expected to become more complex over time, necessitating a deeper understanding of emerging carbon methodologies. We see an interesting trend of developers still submitting data and PDDs using older methodologies and requirements. While this may support projects temporarily in the short term, once the transition to newer methodologies are solidified in the longer term, the pain to make the transition will be inevitably magnified. The time to learn, understand and implement new methodologies is now.
2. Challenges in Scaling
Geospatial and remote sensing algorithms can be challenging to implement and scale. For example, Verra’s latest jurisdictional carbon model and its VT0007 tool/code struggle to scale effectively across entire jurisdictions. To address this, developers and financiers often divide jurisdictions into smaller areas and then reassemble them. However, this approach can reduce model accuracy and increase the risk of human errors.
3. Limitations of Current Tools
We see a strong trend of GIS users experiencing extreme limitations with current tools. While widely used geospatial analytic tools like ArcGIS Pro and QGIS may serve as a starting point, they often fail to handle the latest methodologies effectively. Many users report frequent crashes when working with complex models, large shapefiles, and extensive rasters. Even Google Earth Engine can hit its memory limits under these demands. A shift to more powerful and advanced tech stacks is becoming inevitable.
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